Artemisia vulgaris using Box–Behnken design-response surface methodology (BBD-RSM). The AVM was obtained through a hot water extraction process. The influence of different factors, including pH (U = 4 to 10), temperature (V = 25 to 110 °C), seed/water ratio, i.e., S/W ratio (W = 1/10 to 1/70 w/v), and seed/water contact time, i.e., S/W time (X = 1 to 12 h) on the yield of AVM was evaluated. The p-value for the analysis of variance (ANOVA) was found to be <0.001, indicating that the yield of AVM mainly depended on the abovementioned factors. The highest yield of AVM, i.e., 15.86%, was found at a pH of 7.12, temperature of 80.04 °C, S/W ratio of 1/33.24 w/v, and S/W time of 8.73 h according to Design-Expert Software. The study of the pH-responsive behavior of AVM in tablet form (formulation AVT3) revealed that AVM is a pH-responsive material with significantly high swelling at pH 7.4. However, less swelling was witnessed at pH 1.2. Moreover, AVM was found to be a sustained release material for esomeprazole at pH 7.4 for 12 h. The drug release from AVT3 was according to the super case-II transport mechanism and zero-order kinetics.The current research work focuses on the extraction and optimization of the hydrogel (AVM) from the seeds of  The mucilaginous food polysaccharides/hydrogels of plant seed origin are biocompatible, non-toxic, and environmentally friendly . They arA. vulgaris (Syn: mugwort) is a new source of food hydrocolloid. Different parts of A. vulgaris have been used for the treatment of gynecological issues and gastrointestinal diseases for several centuries [2, and appeared as an important material for different pharmaceutical, biomedical, and industrial applications [The seeds of the enturies ,9. The senturies ,11. AVM ications . HoweverTo extract the hydrogel from plant seeds, the aqueous extraction method has appeared as one of the most common extraction methods . By varyA. vulgaris using hot water extraction. After the collection of preliminarily extraction yield data for AVM, our main focus was to examine the effect of different parameters, i.e., pH (U), temperature (V), S/W ratio (W), and S/W time (X) on the yield (%Y) of AVM in terms of linear, squared, and quadratic functions. The AVM yield data will be verified statistically through BBD-RSM. Moreover, this study also aimed to find the pH-responsive sustained release potential of AVM for the delivery of esomeprazole after compression into a tablet dosage form. The compatibility between AVM and esomeprazole will be verified by recording their FTIR spectra. RSM is one of the most efficient statistical and mathematical tools for the optimization of yield whenever the hydrogel extraction process depends on multivariable factors . It provA. vulgaris seeds absorb water molecules upon soaking in deionized water (DW) due to the presence of micro-pores in them and due to the hydrophilic nature of AVM. The seeds swelled after absorbing DW and released the polysaccharide-based AVM. The AVM appeared colorless in dry powder form. The whole AVM extraction scheme is presented below . Therefore, a pH ranging from 4 to 10 was selected to idealize the optimal pH , medium , and highest . Subjected to these conditions, a BBD-RSM was designed using Design-Expert Software version 11.1.2.1   was obtained at a pH of 7, a temperature of 80 °C, an S/W ratio of 1/30 MN, USA) .The yield of AVM obtained from a series of 29 different experiments was found in the range of 3.45 to 15.75% . The secp-value for ANOVA was <0.001. It means that the BBD-RSM fitted significantly well to the extraction yield data of AVM because the p-values for significant, highly significant, and super significant are <0.05, <0.01, and <0.001, respectively. Moreover, the ANOVA also explained the effect of tested factors on the yield of AVM in the form of linear, quadratic, and interaction terms based on p-values.The results of ANOVA  indicatep-values were found to be <0.001 for pH, 0.0204 for temperature, <0.0001 for S/W ratio, and 0.5215 for S/W time. It shows that both pH and S/W ratio have a super significant effect, temperature has a significant effect, and S/W time has a non-significant effect on AVM yield  indicated that all the factors have a pronounced effect on the AVM yield because the p-values were found to be <0.001 in all cases. The effects of the interaction terms, i.e., S/W ratio and S/W time (WX) was found to be super significant, pH and S/W ratio (UW), and pH and temperature (UV) were found highly significant, temperature and S/W ratio (VW) was found significant, whereas temperature and S/W time (VX) and pH and S/W time (UX) and were found non-significant. The overall order was found to be VX (p = 1.0000) < UX (p = 0.0530) < VW (p = 0.0269) < UV (p = 0.0034) < UW (p = 0.0021) < WX (p = 0.0001)  .R2, R2-adjusted, and R2-predicted were compared to assess the adequacy of BBD-RSM for the extraction yield data of AVM. The difference between R2 (0.9815) and R2-adjusted (0.9630) was found to be 0.0185, and the difference between R2-adjusted (0.9630) and R2-predicted (0.9094) was found to be 0.0536 (R2 indicated that the designed model (BBD-RSM) was adequate and suitable for the AVM yield data [The values of e 0.0536 . These nn R2 0.98 and R2-aA. vulgaris seeds released a high yield of AVM.A useful term for the assessment of the fitness level of any model with high precision in the results, reliability of the results, reproducibility of the results, and the adequacy of the model design is the measurement of coefficient of variance (%CV). If the %CV is less than 10%, then the corresponding model fits well with high precision and hence is highly reliable, whereas if the %CV is greater than 10%, then the corresponding model does not suitable due to low precision and reliability . In the The desirability of the model can also be determined in terms of signal/noise ratio by valuing adequate precision (ADP). Generally, there exists an inverse relationship between the values of ADP and signal/noise ratio and a direct relationship between the values of ADP and model desirability. It means that the greater the values of ADP lower will be the signal/noise ratio, and the greater will be the model desirability. Moreover, 4.0 is the normal value of ADP. If the value of ADP is >4.0, then the model is desirable, and vice versa . In thisPure errors and lack of fit are the two most important characteristics while describing whether a model fits well to the experimental yield data or not. In the case of a significant “lack of fit”, the model design cannot be used to analyze the experimental data, and the response predictor should be neglected. While in the case of a non-significant “lack of fit” the model can be successfully used to analyze the experimental data, and the response predictor cannot be neglected . The valTo analyze the effect of tested factors in binary form, the 3D-RS and 2D-C plots were obtained from BBD-RSM by keeping two factors constant at their optimized values and varying rests of the two factors. The 3D-RS and 2D-C figures indicating the interaction effect of pH and temperature are present in w/v. However, beyond these threshold levels, the AVM yield tends to decrease.w/v, the combined effects of the pH and the S/W time on the AVM yield were investigated. The obtained results are incorporated in At a fixed temperature of 80 °C and an S/W ratio of 1/30 w/v, the yield of AVM also increased. The highest yield of AVM, i.e., 15.46% was obtained at a temperature of 80.31 °C and a S/W ratio of 1/29.85 w/v. After that, the yield of AVM decreased slightly to 13.715% at a temperature of 99.71 °C and the S/W ratio of 1/39.80 w/v.The combined effect of temperature and the S/W ratio was evaluated at a fixed values of pH 7 and S/W time of 8 h  ions. As a result, it offers anion-anion, i.e., –COO− and –COO− electrostatic repulsions, due to which the swelling medium penetrated the polymeric networks of AVT3 and it swelled [The pH-responsive swelling of AVT3 was evaluated at different pH, and the obtained results are incorporated in  swelled . However− form at pH 7.4, and –COO−–COO− repulsive forces operated between the alike –COO− groups. As a result, the inter- and intra-molecular hydrogen bonds that were present at the polymeric backbone of AVT3 were destroyed, and AVT3 swelled abruptly. At pH 1.2, the –COO− groups reverted to the protonated form, i.e., –COOH and AVM regained their intra- and intermolecular hydrogen bonding, which is responsible for its shrinking. The findings of the swelling/deswelling experiments of AVT3 (three cycles) described the reproducibility of the results . The model drug, esomeprazole (European Pharmacopoeia (Ph. Eur.) standard), was gifted by Dyson Research Laboratories, Lahore. The whole experimental work was performed using DW.A. vulgaris were soaked in the DW using an S/W ratio of 1/30 w/v at 80 °C for 8 h. The hydrogel, i.e., AVM, released from seeds was separated by rubbing the swollen seeds on a nylon sieve with the help of a spatula. The non-polar and polar contaminants were removed by purifying the AVM with n-hexane and DW, respectively. The purified AVM was dried in a hot vacuum oven for 48 h at 60 °C. The dried AVM was milled and passed through mesh no. 60 to make finely divided powder. Finally, the AVM was stored in an air-tight jar until further use in the experimental work.The clean seeds of The experimental yield of AVM was determined using Equation (2) :(2)Experw/v), and S/W time (X = 1 to 12 h) on the extraction yield of AVM, some basic experimental studies were conducted. Based on these experimental studies, three levels designated as lowest (−1), medium (0), and highest (+1) were selected for each factor so that the effect of these factors on the yield of AVM in binary form could be monitored. The levels were: U , V , W , and X . The experimental setup consisting of 29 experiments was designed according to the BBD-RSM using Design-Expert Software. The statistical significance of all the tested factors was confirmed from the regression (ANOVA), and graphics (2D-C and 3D-RS) plots for the experimental yield of AVM.Before designing a model for optimizing the best conditions to acquire the maximum yield of AVM and to check the effect of a single independent factor, i.e., pH (U = 1 to 10), temperature (V = 25 to 110 °C), S/W ratio .In addition to the linear as well as quadratic effects, the effect of composite interactions on the yield of AVM was also studied while evaluating the model equation. Therefore, the mean values of the experimental yield data were fitted into a second-polynomial equation to predict the yield and statistical significance of the BBD-RSM. The linearized  second-order equation is represented in Equation (3):p-, F-, 2R, R2-predicted, and R2-adjusted. Moreover, the suitability of BBD-RSM to the extraction yield data of AVM was also estimated in terms of %CV, SSE, ADP, the PRESS, and lack of fit. The 3D-RS and 2D-C plots were obtained to see the connection between the yield of AVM and tested factors and to locate the regions of optimum experimental values of the tested factors and the model desirability. A scattered plot between the Ya and Yp of AVM was drawn to check the adequacy of BBD-RSM for extraction yield data of AVM.The “fitness of good” of the second-order polynomial model equation and ANOVA to the extraction yield data of AVM was evaluated by noting the values of DF, w/v) solution of polyvinyl pyrrolidone (PVP) in isopropyl alcohol and added to the mixture of AVM and esomeprazole to moisten it. This mixture was homogenized, granulated, sieved through mesh no. 12, ground, dried at 40 °C for 8 h, and sieved again by passing through mesh no. 20. The granules were mixed with 10 mg of Mg-stearate and pressed on a rotary press fitted with an 11 mm flat surface punch. The thickness of each tablet was maintained between 6–9 kg/cm2. The composition of each tablet is mentioned in Three tablets of AVM, i.e., AVT1, AVT2, and AVT3, were prepared through a wet granulation method by changing the concentration of AVM in each ,10. AVM −1) spectra of AVM, esomeprazole, and AVT3 were recorded on IR Prestige-21  to evaluate any possible interaction between them.The Fourier transform infrared  is the weight of tea bag having swollen AVT3, eW (g) indicated the weight of an empty wet tea bag, and iW (g) denotes the initial weight of AVT3. The tea bag method  was usedThe swelling/deswelling capability of AVT3 was studied in the swelling medium (pH 7.4) and deswelling medium (pH 1.2). The AVT3 was packed in an already-weighed tea bag and shifted to a beaker containing a buffer of pH 7.4. The swelling of AV3 was noted for 1 h using Equation (4), and then the same tea bag was placed into a beaker containing a buffer of pH 1.2, and the swelling/deswelling capacity of AVT3 was noted using Equation (4). Three swelling/deswelling cycles were recorded thrice, and the means are reported here.The in vitro release study of the esomeprazole from AVT3 was conducted on USP Dissolution Apparatus II at pH 6.8 for a time period of 12 h. A dissolution vessel was filled with 900 mL of the buffer of pH 7.4. The temperature of the apparatus was maintained at 37 ± 0.5 °C. The rotational speed of the apparatus was maintained at 100 rpm. 10 mL of the dissolution sample was taken out from the dissolving medium periodically and filtered. The filtered sample was diluted with the same dissolution medium, and absorbance was noted at 285 nm on a UV-Vis spectrophotometer . The level/volume of the dissolution medium was maintained to its inherent by adding the same dissolution medium. The esomeprazole release study was conducted in triplicate, and the average is reported here.tQ showed the quantity of esomeprazole released at time t, tM/∞M indicated the amount of esomeprazole released at any time t, K0 is the rate constant for the zero-order kinetic model, pK represented the rate constant for the Korsmeyer-Peppas model, and n is the diffusion exponent. The value of n gives information regarding the mechanism of esomeprazole release from AVT3. The value of n ≤ 0.45 is for Fickian diffusion, 0.45–0.89 for non-Fickian diffusion, n = 0.89 for case-II transport, and n > 0.89 for super case-II transport [The esomeprazole release data were fitted to the zero-order kinetic model (Equation (5))  and the ransport ,35.


The primary endpoint was the overall score of nine drills in cycles 1–10. Secondary endpoints included overall, efficiency, and penalty scores in each cycle, as well as the learning curves evaluated by the cumulative sum (CUSUM) analysis. Between September 2021 and May 2022, 20 participants were assigned to the video (n = 10) and control (n = 10) groups. The video group had significantly higher overall scores than the control group . Significantly higher overall scores and lower penalty scores were confirmed, mainly in cycles 1–5. CUSUM analysis revealed a shorter learning curve in the video group. The present study demonstrated that educational video training can be effective in improving the performance of robotic simulation training and shortening the learning curve.The use of virtual reality for simulations plays an important role in the initial training for robotic surgery. This randomized controlled trial aimed to investigate the impact of educational video on the performance of robotic simulation. Participants were randomized into the intervention (video) group that received an educational video and robotic simulation training or the control group that received only simulation training. The The online version contains supplementary material available at 10.1007/s11701-023-01556-4. An increasing number of procedures are being performed as robotic surgery in the fields of gastroenterology, urology, and gynecology . RoboticTraining program in Okayama University for minimally invasive surgery (TAKUMI-1). Furthermore, the effect of the educational video on the learning curve for robotic simulation training was evaluated using a cumulative sum (CUSUM) analysis.This study aimed to investigate the impact of educational video on performance of robotic simulation training, called the We performed a randomized controlled trial with two parallel training groups; one group received an educational video and robotic simulation training (video group), while the other received simulation training alone (control group). The study protocol was approved by the institutional ethics committee of No: 2108–017) and was registered at the University Hospital Medical Information Network (No: UMIN000045495). The Consolidated Standards of Reporting Trials (CONSORT) guidelines were followed [–017 and Robotic surgery trainees in our department were recruited between September 2021 and May 2022. The participants included experienced surgeons and surgical trainees who had no experience with robotic simulation training.da Vinci® Skills Simulator . The da Vinci® Skills Simulator for the basic course, including nine drills, was used for this trial: drill 1, sea spikes 1; drill 2, sea spikes 2; drill 3, camera targeting 1; drill 4, suture sponge 1; drill 5, thread the ring; drill 6, energy switching 1; drill 7, ring and rail 1; drill 8, needle targeting; and drill 9, 30° scope swap (Supplementary Table 1). The exercise goals for each drill are listed in Supplementary Table 1.This study was performed using a Educational videos on the tips and tricks of each drill were created by the principal investigator (KT) (Supplementary Table 1). The participants in both groups received robotic simulation training. However, the video group received an additional educational video training for a few hours prior to starting the robotic simulation. All participants had to perform the nine drills consecutively as one cycle, and complete 10 cycles in total.Assessment outcomes for each drill included overall, efficiency, and penalty scores. Efficiency scores were calculated by evaluating the time to completion, economy of motion, or master workspace range. Penalty scores consisted of excessive force use, instruments out of view, drops, instrument and cone collisions, incorrect rings, missed targets, and misapplied energy time. The overall scores were calculated by subtracting the penalty from the efficiency scores.The primary endpoint was the overall score of all drills in cycles 1–10 in both groups. The secondary endpoint included the overall, efficiency, and penalty scores in each cycle and drill, as well as the learning curves between the groups.The sample size was calculated based on the primary end point. Assuming a difference in the mean overall scores between the groups of 15 with a standard deviation of 10 according to our unpublished data, this study required a total sample size of 20 participants (10 participants for each group) to achieve a power of 90% and an alpha error of 5% (two-sided).Randomization was performed by the principal investigator using a random number (Excel RAND) function. The participants were randomly divided into two groups: video and control.t test or Mann–Whitney U test for continuous variables. Statistical significance was set at P < 0.05.Statistical analysis and sample size calculation were performed using the JMP 11.2.0 software . First, the overall, efficiency, and penalty scores of all drills in cycles 1–10 were compared between the video and control groups. Subsequently, the overall, efficiency, and penalty scores in each cycle as well as each drill were analyzed and compared between both groups. Moreover, the detailed penalty scores for each drill were compared between the groups. Finally, a CUSUM analysis was performed to compare the learning curves between the groups. The cumulative sums of the differences in each drill from the total “time to completion” were calculated based on the cycle. A pooled mean CUSUM was plotted to summarize the results , 10. Datn = 10) or the control (n = 10) group (Table n = 10) or hepatopancreatobiliary surgery (n = 10). All participants completed 100% of the curriculum in both groups.The CONSORT flow diagram is shown in Fig. P < 0.001). The overall, efficiency, and penalty scores of all drills in cycles 1–10 between the video and control groups are shown in Table The overall, efficiency and penalty scores in each cycle between the groups are shown in Fig. The CUSUM analysis of the total “time to completion” in each drill between the groups is depicted in Fig. This is the first randomized controlled trial to investigate the impact of educational videos on the performance of robotic simulation training. We found that educational video training improved the performance in robotic simulation. Moreover, CUSUM analysis confirmed the positive effect of the educational video on shortening the learning curve.Educational video should be exemplary for early training in robotic surgery and have the potential to maximize trainees’ learning and skill improvement . Video tRegarding the evidence on robotic simulation training, several randomized controlled trials have been performed to investigate the effect of an affordable surgical robot simulator or procedural virtual reality, showing effective impact in improving robotic surgical skills , 16. HowWe determined that the overall scores of all drills, would be the best index for estimating the impact of educational video training. The mean overall scores in the video group were 18.4 times higher than those in the control group, probably due to educational video training.Participants in the control group improved their overall, efficiency and penalty scores through self-learning, and eventually reached the same level as that of the video group. However, the video group had significantly higher efficiency and lower penalty scores, especially during the initial phase. We propose that participants in the video group had improved their scores through both educational video training and self-learning. The penalty scores in the video group were lower throughout the drills, especially in collisions and out-of-view instruments Below is the link to the electronic supplementary material.


It is generally believed that time flows in one direction and that a reversal of time's arrow would render the external world non-sensical. We evaluated our ability to tell the direction of time's arrow in a wide range of dynamic scenes in our daily life by presenting 360 video clips in the correct or incorrect direction. Participants, who judged the direction in a speeded manner, erred in 39% of trials when a video was played in reverse, but in only 9% when it was played normally. Due to the bias favouring the ‘forward’ judgement, the reaction was generally faster for the forward response. However, the reaction became paradoxically faster and more synchronous for the detection of reversal in some critical occasions such as forward motion, free fall, diffusion, division and addition of materials by hand. Another experiment with a fraction of the video clips revealed that reversal replay of these videos provided instantaneous evidence strong enough to overtake the forward judgement bias. We suggest that our brain is equipped with a system that predicts how the external organisms behave or move in these critical occasions and that the prediction error of the system contributes to the fast ‘reversal’ detection. We substitute milk with water in Japanese, saying ‘spilt water does not return to the vase’. The Japanese proverb did not originate in Japan but was borrowed from a historical document in ancient China. The existence of these proverbs tells us that it is universal and long-lasting knowledge, or belief, that time flows in one direction and not in reverse. In the twentieth century, Sir Arthur Eddington, a British astrophysicist who introduced the phrase ‘time's arrow’, noted that ‘our reasoning faculty tells us that a reversal of the arrow would render the external world nonsensical’ . Indeed,It is the proper direction of time's arrow that underlies our judgement of the order of any events in time. It is generally accepted that we are able to judge the order of successive stimuli when they are separated by 20–30 ms ,5 or priIn the present study, we aimed to evaluate our ability to tell the direction of time's arrow in a wide variety of natural scenes. For this purpose, we used 360 short video clips, 3 s in duration, in the ‘Moments in Time Dataset’ , which i. 2. 2.1Thirty-seven healthy adults (aged 20–27 years), 10 for Experiment 1 and 27 for Experiment 2, participated. All participants were right-handed and had normal or corrected-to-normal visual acuity. All participants provided written informed consent. The study was approved by the ethics committee of the Graduate School of Frontier Biosciences of Osaka University, Japan.. 2.2n = 339) and 21 more (one from each of 21 randomly chosen classes) to cover a wide variety of dynamic scenes of daily life. In choosing one from each class, our choice was essentially random, except that we excluded videos with panning, zooming or scene changes. Each video clip was presented on a tangent liquid crystal display  in either forward (from frame No. 1 to 90) or reverse (from No. 90 to 1) direction after presenting the first frame (No. 1 or No. 90) for 1.5 s (a). Participants, seated on a chair with their head resting on a chin rest, viewed each video  on the display placed at a viewing distance of 40 cm and judged whether each video clip was played in the forward or reverse direction. Participants were required to respond as soon as they reached a judgement by pressing one of two buttons that were assigned to the index finger (forward) and the middle finger (reverse) of the right hand (a). They were allowed not to respond when they did not reach a judgement, but they responded in 98.8% of trials within 5 s of the movie onset. Each video clip was played twice, once in the forward and once in the reverse direction, in two different sessions that were performed on two different days. Participants performed 360 trials per day, in which half (n = 180) were played in forward and the others were played in reverse. Permutations were randomly determined for each participant.In Experiment 1, we used 360 video clips  that were selected from the ‘Moments In Time dataset’ . The dator 1.5 s a. Particght hand a. They wb). Each video clip was divided in three different ways into 10 or 11 fractions: type (1) {Frame 1–9}, {10–18}, … , {82–90} (10 fractions), type (2) {1, 1, 1, 1–6}, {7–15}, … , {79–87}, {88–90, 90, 90, 90, 90, 90, 90} (11 fractions), and type (3) {1, 1, 1, 1, 1, 1, 1–3}, {4–12}, … ., {76–84}, {85–90, 90, 90, 90} (c). Here, {1, 1, 1, 1–6} means that there were three Frame 1's, three fillers, before Frames 1, 2, 3, 4, 5 and 6. Frame 1 was used as fillers of three empty frames because Frames 1–6 cannot fill all nine frames. Likewise, Frame 90 was used as fillers of the right-most empty frames when necessary. The borders of divisions were different by three frames (100 ms) to improve the overall temporal resolution from 300 to 100 ms. For each of the 120 video clips, nine participants were assigned, and three were assigned to each of the three types of divisions (3 × 3 = 9). To reduce the number of trials for each participant, we recruited 27 participants and showed a part of the 120 video clips, 40 (one-third of 120) on average, to each participant. To be more precise, nine participants (Group 1) were assigned to 29 video clips (14 evident and 15 ambiguous), nine (Group 2) to 45 (26 evident and 19 ambiguous) and the other nine (Group 3) to 46 (26 evident and 20 ambiguous) video clips. Each participant participated in two sessions over two consecutive days and made judgements in 380 to 506 trials per day. The number varied according to the type of division and video clips assigned to each group of participants. The order of presentations was randomized for each participant. The total number of trials was 23 040 (= 120 video clips × (10 + 11 + 11) fractions×2 directions × 3 participants). In some stimuli, there was actually no movement at all, in which case participants were allowed to press both buttons to express that they did not see any movement. Participants responded by pressing one of the two buttons in 95.6% (n = 22 018) of the trials (n = 23 040).In Experiment 2, we chose 120 video clips from the 360 clips used in Experiment 1. Sixty-six of the 120 video clips were video clips with perfect judgement across the 10 participants in Experiment 1 (evident videos). The other 54 clips were chosen from 63 video clips in which the correct judgement rate was equal to or smaller than 0.5 (ambiguous videos). Only a fraction  of the video clips were presented in each trial, and participants were required to judge the direction and respond during a period of 2 s after each presentation b. Each v 90, 90} c. Here, Tasks were controlled using MATLAB  and Psychtoolbox , which were run on a Windows PC . The timing of the button press was acquired at a sampling rate of 1000 Hz using a USB input/output board  connected to the PC.. 2.3d′) and response bias (positive for the bias favouring the forward judgement) by using the formula as follows:norminv denotes the normal inverse cumulative distribution function. We substituted 0.01 and 0.99 for 0 and 1, respectively, to prevent the values from diverging to infinity. These values were calculated for each video clip and for all 360 video clips as a whole.In Experiment 1, we calculated the probability of correct judgement, p(f|f) and p(r|r), for each of the 360 video clips by accumulating responses from 10 participants. Here, p(f|f) denotes the probability of the ‘forward’ response in response to ‘forward’ presentation. By applying signal detection theory  to p(f|fd′ > 4) and ambiguous videos with an overall correct judgement rate of 0.5 or smaller (p(f|f) + p(r|r) ≤ 1, d′ ≤ 0). The reaction time (RT), measured from video onset, was accumulated across the participants for each of 2-by-2 groups of trials: (evident or ambiguous) × (forward or reverse responses). Distributions of the RT were fitted by the Rayleigh distribution with a fixed delay for each group to see if there were any significant differences in the speed of judgement. Two parameters  were estimated using maximum-likelihood estimation. We also estimated 95% confidence intervals of the peak RT. The lack of overlap between two 95% confidence intervals was used as a good indicator of separation because the two groups could be separated with specificity greater than 0.975 and sensitivity  greater than 0.975. We additionally fitted each distribution of RT using a diffusion model with a single threshold  and a normal distribution  s . The peak RT increased in the order of 1.38 s , c.i. = [1.35 1.41] s), 1.85 s , and 2.01 s . These 95% confidence intervals had no overlaps in any of six combinations. Furthermore, the difference in their medians was highly significant in 5 of 6 combinations , with an effect size of 0.59 − 0.83 (AUC), although the difference was still significant in the remaining combination .These observations, including paradoxically shorter and synchronous reverse responses in the evident video clips, were reflected in the distributions of the RT a–d, whice). As expected from the example data in p < 10−5, AUC > 0.73), except for the (f|f) and (r|r) responses in the ambiguous trials .We further compared the standard deviation of the correct responses (f|f and r|r) across the evident and ambiguous video clips e. As expb). The evidence score took a value from −1  to +1 .We have shown so far that the reverse responses were faster in the evident video clips in spite of the general bias toward the forward responses. However, this could have resulted from asymmetry in the distribution of evidence: critical events, like the evident splash in video No.1, could have occurred in the later part of the video when it was played in the normal direction. To illustrate the dynamic changes in the strength of evidence, we conducted Experiment 2, in which participants viewed only a 300 ms fraction of the 3 s video clip b. The evb) but showed a marked and transient decrease to −1 (red trace in figure 3c) when it was played in reverse, just before the ten participants in Experiment 1 made unanimous judgements of reversal. The unnatural convergence of the splash that occurred within a period as short as 300 ms was sufficient for the participants in Experiment 2 to make a unanimous judgement of reversal. As for the evident video clip of the tropical fishes (No. 22), the evidence score reached +1 (or −1) as soon as the replay started in the normal (or reverse) direction . By contrast, the evidence scores for ambiguous clip No. 122 remained greater than 0.7 most of the time, whether it was played forward or in reverse .As expected, the evidence scores for evident clip No. 1 remained positive when it was played forward . However, when the evidence score was averaged around the time of response, it was clear that the median evidence scores increased to +1 (blue) or dropped to −0.5 (red) approximately 0.5 s before making the forward or reverse response (b). As for the ambiguous movies, the median evidence score lingered around +0.7 whether they were played in the normal or reverse direction (c). Separation of the scores before the responses was minimal (d).When the evidence scores were averaged across the 66 evident video clips, the median evidence scores were almost flat across the replay period whether they were played in the normal or reverse directions a. Howeveresponse b. As forirection c. Separa minimal d.Figurefh for the forward and rh for the reverse response) whose slope is modified by the evidence score as follows:By using the evidence score obtained in Experiment 2, we introduced a hazard rate function  at which the hazard rate functions simply correlate with time and yield Rayleigh distributions with a peak at fb and rb. We fitted the hazard rate model to the data of 120 video clips in Experiment 1 using maximum-likelihood estimation. To ensure the generalizability of our data fitting, we used the leave-one-out technique. The estimated parameters were 0.43 ± 0.032 (mean ± s.d.) for the neutral evidence score (c), 0.46 ± 0.000038 s for the delay, and 1.7 ± 0.0046 s and 3.0 ± 0.0013 s for fb and rb. The coefficient fk was 0.043 ± 0.00047, and rk was 0.040 ± 0.00032.Here, b,c). The resulting response probability functions were similar to the Rayleigh function with a peak at approximately 1.4 s and actual responses distributed according to the predicted distribution. The hazard rate function with a strong bias toward forward judgement  captured the Rayleigh-like distribution of RT for the forward responses in general.When ambiguous video No. 122 (cats eating) was played, the evidence score remained above neutral bias whether it was played in the forward or reverse direction b,c. The b), the evidence score varied around the neutral bias ; thus, the response probability density function for the forward judgement was almost identical to a Rayleigh function with a peak at approximately 2 s . The actual responses were distributed widely over the predicted distribution. In marked contrast, when evident movie No. 1 was played in reverse (figure 3c), the evidence score dropped sharply to −1, and the difference from the neutral bias was as large as 1.43. This resulted in a sharp increase in the hazard rate function for the reverse response ) with a delay of 0.46 s, which resulted in the formation of a sharp peak of response probability for the reverse judgement at approximately 1 s (figure 3e).When evident video clip No. 1 was played forward b, the eve) than for the forward replay with a dull peak similar to those for the ambiguous video (d). Actual responses of ‘reverse’ occurred within the sharp peaks, showing that the hazard rate function model with a modification term could predict the nearly simultaneous judgement of ‘reversal’ of time's arrow in the evident video clips.When video clip No. 28  was played, evidence score was stationarily saturated to +1 (forward) or −1 (reverse) in a symmetrical manner. However, the model predicted faster and more synchronous responses for the reverse replay with a sharp peak of probability density at approximately 1 s e than fous video d. Actualn = 66) one by one, we noted five categories of motion: (1) forward motion of people, animals, or vehicles, (2) free fall, or ballistic motion, under gravitational force, (3) centrifugal diffusion or explosion of small particles, (4) division of some material by hand or a tool and (5) addition, or construction of some material on others by hand. We list examples of videos in each category of motion in figure 8a. On the other hand, we noticed that many ambiguous videos contained reciprocating motion, such as head, body and mouth motions while eating (e.g. No. 122) or in conversations, hand and body motions while playing musical instruments, swinging of swings, oscillations of mobiles, etc. .However, we still did not know what kind of motion could serve as critical evidence for the reversal of time's arrow strong enough to overturn general bias favouring forward judgement. By observing the evident videos (12), 0.61 (r23) and 0.63 (r13) and were highly significant (p < 10−200). The six scores, averaged across raters, were used to predict the d′ of each of the 360 video clips. A linear model with the six parameters and a constant was fitted to the data (b). As shown by the estimated coefficients and the 95% confidence intervals (c.i.), all six categories of motion significantly contributed to the prediction of d’. Five served as significant cues to help discrimination: (1) forward motion , (2) free fall , (3) diffusion , (4) division  and (5) addition . On the other hand, (6) reciprocating motions served as a significant confounding cue . It is noteworthy that the contribution from the forward motion was significantly greater than free fall and diffusion but was comparable to division and addition by hand.Three experimenters rated the 360 videos as to whether they included each category of motion (1) or not (0). The correlations of the ratings between different raters were 0.67 (rthe data b. As sho. 4g). However, this was not always the case. Participants reported reversal of the arrow faster and more synchronously than they reported normalcy as long as the 66 evident videos with a few critical cues, forward motion, free fall, diffusion, division and addition were played (figures b). Individuals were better able to notice reversal of time's arrow, in situations with the five critical cues in particular, than they were to notice that time is flowing in the normal direction.In the present study, we used 360 short video clips to test whether individuals are actually able to judge the direction of time's arrow. Participants were able to judge the correct direction in 75% of trials, but there was a clear asymmetry: the correct judgement rate was much greater in the forward replay condition (p(f|f) = 0.91) than in the reverse replay condition (p(r|r) = 0.61). The response bias favouring the forward judgement resulted in faster forward responses in general g. Howeve figures . Linear  figures b. Indivi. 4.1It may be worth clarifying why a response bias favouring one type of response leads to faster responses for the favoured response. Assuming a standard diffusion theory, the bias is expressed as a bias of the starting point (nearer to the threshold of the favoured response) or a bias in the speed of diffusion (greater speed of diffusion toward the favoured response) ,18. In e. 4.2b, which represented a peak of RT. The parameter was 1.7 s for the forward response (fb = 1.7 s), nearly half of the parameter for the reverse response (rb = 3.0 s). The reverse of the parameter represented the slope of the hazard rate function that was nearly twice as large for the forward response (1/1.7 [1/s]) than reverse responses (1/3.0 [1/s]). Additionally, the diffusion model we used for comparison yielded a sigmoid hazard rate function, and the linear hazard rate function with a delay (of the Rayleigh model) captured the gist of the sigmoid hazard rate function yielded by the diffusion model. We thus started from the simple linear hazard rate function with a delay, without assuming any hypothetical diffusion process in the background.We did not adopt the diffusion model but adopted a Rayleigh-like hazard function model because the Rayleigh model fit generally better to the distributions of the RT . In our model, the response bias favouring the forward judgement was simply captured by a single parameter, . 4.3c)/2, where c = 0.47) by default. When the content of the video provides no more or no less evidence than the default baseline, the hazard rate of response increases in proportion to time after a delay of 0.46 s. The slope of the hazard rate function for the forward response (1/fb = 0.58 [1/s]) is greater than that for the reverse response (1/rb = 0.33 [1/s]) and yields the Rayleigh distribution with a peak at fb = 1.7 s for the forward response and another with a peak at rb = 3.0 s for the reverse response. These ‘baseline’ responses are reflected in the distribution of RT for the ambiguous videos shown in figure 6b (forward response) and fb + delay), whereas they respond ‘reverse’ in 27% with a longer peak delay of approximately 3.4 s (= rb + delay).We can explain how the reversal response overtook the forward response bias in evident video clips using the hazard rate model we developed in the present study (equations (2.3) and (2.4)). When we see a video in which something or somebody moves, we expect that the video is flowing in the normal direction (forward) with a probability of 0.73 (= (1 + a). However, it is worth noting again that such strong evidence is not always available but is provided in some limited occasions, which were categorized into five, in the evident videos.However, the evidence score dynamically changes over time from the baseline of 0.43. Notably, the dynamic range for the ‘reverse’ limit of −1, 1.43, is 2.5 times as large as the dynamic range toward the ‘forward’ limit of +1 (0.57). In other words, there is not much room for enhancing the forward judgement due to the baseline bias toward forward, but there is much greater room for enhancing the reverse judgement. The asymmetry in the dynamic range explains why participants made unanimous and synchronous judgements of ‘reverse’ when a critical cue with a strong evidence score of reversal was presented . However, when the evidence scores were averaged across all the video clips, we found that these cues were distributed almost evenly over the 3 s whether it was played forward or in reverse direction (a). Moreover, when we averaged the evidence score around the time of response, we observed that the median evidence scores increased to +1 (blue) or decreased to −0.5 (red) approximately 0.5 s before making the forward or reverse response, respectively (b). These results indicate that participants responded accurately to the evidence, which could be located at any arbitrary position within the video clips. Based on these findings, we constructed a quantitative model, and we objectively estimated the delay of response from evidence presentation to be 0.46 s.In our first experiment, we presented natural video stimuli that were 3 s long and occasionally contained critical cues that aided participants in judging the reversal of time's arrow. However, unlike artificial visual stimuli, such as random dot motion stimuli, these cues or evidence for judgement were not uniformly distributed over time and could not be controlled by the experimenter. To overcome this difficulty, we conducted a second experiment in which we assessed dynamic changes in the strength of evidence over 3 s by displaying a fraction of the video (300 ms in duration) to 27 participants. The evidence scores of each video clip displayed a wide range of temporal profiles, with some showing a sharp peak or trough at the beginning of the replay in reverse e.g. c. Howeveirection a. Moreovectively b. These b,c). In spite of the symmetric distributions of evidence, the model predicted faster responses to the reverse replay with a peak at approximately 1 s  compared to the responses to the forward replay . The asymmetric predictions, which largely depended on the asymmetry in dynamic ranges from the neutral evidence score of 0.43, were verified by the responses of the participants that matched well with the predicted curves of response probability density. It is important to note that the dataset from the video featuring tropical fishes itself was not used for the prediction but just for cross-validation. We expect that the model can predict responses to a range of natural visual stimuli, provided that the temporal profile of the evidence score is available. However, the generalizability of the model needs further verification in the future.It is uncertain whether the model developed using evidence scores and responses obtained from 120 video clips can predict responses to novel stimuli. To address this issue, we adopted a leave-one-out procedure, where we excluded a dataset in question from each fit. For example, we take the model predictions for the video clip featuring tropical fishes swimming in the sea . The evifigure 4. 4.5Eddington  wrote tha) but was later expanded to the prediction of any mental process in the cerebral cortex, such as mentalizing of others [figure 9b). With this schema in mind, we notice that all five critical cues for reversal detection can be regarded as prediction errors regarding actions or intentions of others  or movements of non-living material . We infer that judgement of normalcy was slower because there was little prediction error, and slower conscious reasoning had to be recruited.Then, what is the use of the ‘reversal’ detector when time's arrow never reverses in the actual environment? We propose that the ‘reversal’ detector is not designed to detect reversal per se but to yield a prediction error in these critical occasions . The configure 9f others ,23 (figuTime's arrow, the subjective ordinary direction of time, is likely to be supported by the acquired predictors and comparators in the brain that are always at work behind the scenes to predict actions or intentions of others. The prediction errors, which are ultimately provided to the cerebellum by way of the inferior olivary nuclei ,23, are


In recent years, video games have been on the rise as entertainment goods, leading to a growing interest by practitioners, researchers, and, of course, consumers alike. While a few unusually successful video games produce overall high revenues, most released games struggle to break even. Hence, there is an urgent need to better understand what distinguishes financially successful games from nonsuccessful video games. Accordingly, several researchers have called for investigations into the drivers of the financial success of video games. However, empirical studies within this respect are still lacking. Based on longitudinal data of 351 video games, the current study strives to fill this research gap by investigating the relative importance of potential success factors for the short-term and long-term financial success of video games. The results of multiple regression analyses confirm that search qualities such as brand popularity, reviews, and awards as well as experience qualities such as graphics, sound, and game duration significantly drive financial success in terms of the total number of sold video games in Europe. Consequently, managers in the video game industry can boost their chances for the production of a successful video game by focusing on these factors. The video game industry generated US$174 billion in worldwide revenue in 2020, which represents a growth of 19.6% from the previous year  is readily available to consumers prior to purchase without consuming the respective offer  “have to be experienced by the consumer before he or she is able to evaluate them”  and the GameStar review scale  that were published between 2010 and 2015 as well as reviewed in the largest (highest circulating) German game magazine “GameStar” DWDL, . FollowiWith respect to experience qualities, review data from the highest circulating German Gaming magazine, namely, The GameStar DWDL, , were coFor statistical analysis, multiple regression analyses using SPSS 27.00 were run for both research models by applying mean replacement as the missing value algorithm. For hypothesis testing, regression coefficients and their significances were assessed for each research model, further subdivided into a short-term and long-term success model.As the research model on search qualities exclusively consists of constructs with a single indicator, we only checked for the discriminant validity of the constructs. As Table F = 10.781, p < 0.01) and long-term success  = 7.161, p < 0.01), indicating that all three factors have a significant impact on video game success. Furthermore, the data fit the model well, with an R2 of 0.201 for short-term success and 0.143 for long-term success. To check for multicollinearity, the variance inflation factor (VIF) was calculated for both dependent constructs. As the highest VIF value was 1.530, no multicollinearity exists , partly confirming hypothesis 1. Contrary to our predictions in hypothesis 2, production costs did not significantly affect short-term success  nor long-term success . In line with hypothesis 3, reviews positively affected both short-term  and long-term success . Star factor affected neither short-term success  nor long-term success , leading to a rejection of hypothesis 4. Confirming hypothesis 5, awards exhibited a positive effect on both short-term  and long-term success . Contrary to our predictions in hypothesis 6, platform availability negatively affected short-term success  as well as long-term success . In line with hypothesis 7, brand popularity positively affected both short-term  and long-term success . Finally, the inclusion of a multiplayer mode positively influenced short-term  and long-term success , confirming hypothesis 8.Regarding developer popularity as a search quality, our results show no significant effect on short-term success  = 6.663, p < 0.01) and long-term success  = 11.071, p < 0.01), indicating that all three factors have a significant impact on video game success. Furthermore, the results confirm a sufficient fit of the estimations and data with an R2 for short-term success of 0.054 and for long-term success of 0.087. For both dependent constructs, the highest VIF value was 1.914. Hence, no multicollinearity should be present  but insignificant for short-term success . According to the results, hypothesis 10 is rejected, as the suggested positive impact of ease of use on both short-term  and long-term success  turned out insignificant. However, support was found for hypothesis 11, as content positively affected both short-term  and long-term success .As shown in Table For an industry that is projected to grow by 7.17% 2023–2027), resulting in a market volume of US$482.30 bn in 2027  German game magazine “GameStar” DWDL,  between Third, we used only reviews from the most popular German game magazines for the corresponding analysis of experience qualities. While ratings in expert magazines are often quite similar, sometimes there are some differences as a result of subjective expert perception. Accordingly, future studies might validate our findings on the contribution of experience qualities to video game success using reviews from other game magazines and even from other countries.Fourth, to formulate an initial theoretical framework for video game success factors with a high level of internal and external validity, we relied on secondary data from a period of time when external circumstances such as financial crises or pandemics did not drastically influence consumer purchasing or usage behavior. However, future research might draw on our theoretical framework and investigate whether generated findings during a rather stable time period might be affected by different external shocks that have taken place since that period, such as the COVID-19 pandemic. More specifically, it would be interesting to investigate whether the relative importance of different search and experience qualities may have changed during the COVID-19 pandemic. In doing so, our newly formulated framework would gain in external validity and be extended by potential boundary conditions.Fifth, in our study, we strived to provide the most comprehensive picture of the potential success factors of video games. We thus investigated the impact of 8 search and 3 experience qualities on video game success. Although we included the most important established success factors from other entertainment sectors and derived a sufficient number of video game-specific success factors, there may be additional factors that were not considered in our analysis. Hence, future research may search for other success factors in the video game industry, empirically validate their effectiveness, and extend our theoretical framework by adding more search and experience qualities.Sixth, success for entertainment products emerges due to a constant revenue stream over time, such that our main focus was to assess the economic impact of several search and experience qualities as video game success factors over time. Nevertheless, nonmonetary success dimensions are crucial areas to consider when investigating success factors, including effects on brand perception in terms of innovation and building a strong community of players who co-create and support the video game through updates. Future research might rely on our theoretical framework and investigate whether our identified search and experience qualities also account for nonmonetary success and whether their relative importance might vary compared to nonmonetary dependent variables.Seventh, the main focus of our study was on investigating the success factors of video games. While identifying potential success factors is an important first step in understanding what makes a video game successful, it is also important to consider factors that may have a negative impact on success, such as errors or a lack of hotfixes. Future studies might therefore focus on barriers to video game success to complement the picture of what constitutes a successful video game.Aside from the mentioned limitations, we suggest the following avenues for future research. First, future research could investigate whether the linkage to other media products, e.g., motion pictures or books, has an influence on the success of video games (Apperley, In conclusion, this study provides a theoretical framework to classify and anchor success factors in entertainment industries, specifically video games, based on the economics of information theory. The study confirms the applicability of established search qualities from the movie industry context, such as developer popularity, reviews, and awards, as success factors in the video game sector. However, the study also suggests that other established success factors, such as production costs and star factors, do not seem readily transferable to the context of video games. This study further advances our understanding of potential success factors in the entertainment industry by demonstrating the need for tailor-made success factors for the respective entertainment context, especially with respect to experience qualities such as presentation and content. Overall, the resulting framework of search and experience qualities establishes a common ground that enables future empirical investigations into the success factors of the entertainment industry and provides new insights into the mechanisms that drive the success of entertainment products, which may guide future research in this field.


Ward rounds are an essential component of surgical and perioperative care. However, the relative effectiveness of different interventions to improve the quality of surgical ward rounds remains uncertain. The aim of this systematic review was to evaluate the efficacy of various ward round interventions among surgical patients.A systematic literature search of the MEDLINE (OVID), EMBASE (OVID), Scopus, Cumulative Index of Nursing and Allied Health (CINAHL), and PsycInfo databases was performed on 7 October 2022 in accordance with PRISMA guidelines. All studies investigating surgical ward round quality improvement strategies with measurable outcomes were included. Data were analysed via narrative synthesis based on commonly reported themes.n = 25), followed by randomised controlled trials (n = 3). Checklists/proformas were utilised most commonly (n = 22), followed by technological (n = 3), personnel (n = 2), and well-being (n = 1) quality improvement strategies. The majority of checklist interventions  showed significant improvements in documentation compliance, staff understanding, or patient satisfaction. Other less frequently reported ward round interventions demonstrated improvements in communication, patient safety, and reductions in patient stress levels.A total of 28 studies were included. Most were cohort studies (Use of checklists, technology, personnel, and well-being improvement strategies have been associated with improvements in ward round documentation, communication, as well as staff and patient satisfaction. Future studies should investigate the ease of implementation and long-term durability of these interventions, in addition to their impact on clinically relevant outcomes such as patient morbidity and mortality.The online version contains supplementary material available at 10.1007/s00268-023-07221-z. Ward rounds are an essential component of surgical and perioperative care . They alRegulatory bodies have provided expected standards of communication and documentation in doctor-patient consultations . MultiplWard round checklists and proformas have been developed in an attempt to improve patient care through better documentation of patient progress and management plans –12. StudCurrent literature demonstrates a wide variety of different interventions to improve the quality of surgical ward rounds , 13–15. The protocol for this review was prospectively registered on PROSPERO (ID: CRD42022359414) . The revA systematic literature search of the MEDLINE (OVID), EMBASE (OVID), Scopus, Cumulative Index of Nursing and Allied Health (CINAHL), and PsycInfo databases was performed 7 October 2022. The search string consisted of key words and Medical Subject Headings (MeSH) terms for various surgical specialties , medical staff members , and ward rounds , among others. These terms were combined using the ‘AND’/‘OR’ Boolean operators (refer to Supplementary Appendix S2 for an exemplar search string using the MEDLINE database).Databases were searched from their date of inception. The results were restricted to studies published in English. There were no limitations on patient age, geographic location, or study design. Reference lists of included studies and relevant systematic reviews were also hand-searched to identify additional studies for inclusion.All original studies investigating quality improvement strategies implemented during an inpatient surgical ward round were eligible for inclusion. Surgical ward round was defined as any setting or situation in which member(s) of a surgical team assessed patients as part of perioperative care, regardless of surgical specialty. Only studies reporting quality improvement interventions with a measurable outcome on an individual patient  or hospital/department , and those where the majority (> 50%) of included patients were under surgical care, were included.We excluded case reports (with< 5 patients), articles without an accessible full-text and/or conference abstracts without a full-text publication. Reviews and studies published in languages other than English were also excluded.Article records from the database searches were exported into EndNote X9  and de-duplicated using validated methods . Two invRelevant data from included studies were extracted into a proforma Google spreadsheet by a single investigator (RH). These data were independently validated by a second investigator (SB), with any disagreements resolved via mediation with a senior investigator (CW) until consensus was reached. Extracted data comprised study characteristics, conflicts of interest, study funding, surgical specialty, number and designation of medical staff involved, sample size (pre and post-intervention), description of intervention and method of implementation, as well as the comparator intervention. Individual patient and/or hospital/departmental level outcomes were also extracted. Data that were reported in the form of graphs and/or figures were extracted using WebPlotDigitizer  . AttemptTwo authors (RH and SB) independently performed methodological quality assessment of included studies, with disputes resolved through discussion. The Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I) tool  and JoanData were analysed via narrative synthesis according to major reported themes among the included studies. Simple descriptive statistics were used to quantitatively report data where possible.The database search yielded a total of 3362 results, from which a total of 28 studies were included in the qualitative synthesis Fig.  3, 5, 9, 26–46.Fn = 25, 89.3%), followed by RCTs . Most studies were conducted in the UK , followed by the USA , and Aotearoa New Zealand . All were single-centre studies, including patients from a range of surgical specialties; general  and orthopaedic surgery , surgical intensive care unit , trauma surgery , and urology  were the specialties assessed most frequently.Characteristics of the 28 included studies are provided in Table . Four prospective cohort studies were judged to be at critical risk of bias, principally due to the impact of unmeasured confounding variables [n = 2 studies, 66.7%) [Quality assessment results using the ROBINS-I tool are depicted in Supplementary Figure S1ariables , 34, 38 , 66.7%) , 14, resn = 1) [n = 1) [n = 1) [n = 1) [n = 1) [n = 1) [A range of quality improvement interventions were implemented during surgical ward rounds. In total, 22 studies used some form of a ward round checklist or proforma (refer to Supplementary Appendix S4 for an example of a ward round checklist). Other interventions included a ‘surgeon of the week’ rounding system (n = 1) , additio [n = 1) , involve [n = 1) , digital [n = 1) , mobile  [n = 1) , and imp [n = 1) .Checklists and proformas served as a guidance for information that should be covered in a surgical ward round, or a template to ensure adequate documentation of essential ward round points. Of the 22 studies that employed a checklist or a proforma, most introduced physical stickers or forms which were placed in a patient’s medical record , whereas information printouts displayed throughout the ward were trialled in three studies  cycle design, whereby ward round interventions were iteratively reviewed and improved after each study , 27, 32.. [Yorkgitis et al.  introduc. [Baker et al.  reportedPre- and post-intervention surveys were completed by staff and patients to measure satisfaction levels. Generally, ward round quality improvement strategies were well received by staff and patients. Two studies found that checklists had utility as a tool for learning and guiding ward round documentation , 21. KriNon-checklist interventions also elicited positive responses. Interventions such as the institution of active breaks during the surgical ward round , adjunctn = 10) preferred a proforma to standard ward rounds due to improved clarity of information. Banfield et al. [. [Five studies investigated the impact of checklist interventions on communication between staff and patients. Alazzawi et al.  reportedd et al.  demonstrd et al.  observedt al. [.  demonstrt al. [. . [Significant reductions in overall ward round duration were observed through the use of mobile tablet technology  and a wa.  found th. , 45.Only two studies investigated the impact of ward round interventions on perioperative patient outcomes , 3, 14. . found that compliance was low six months post-intervention (75% proforma usage), whereas Alamri et al. observed comparatively higher compliance with documentation criteria up to two years post-intervention (> 80% completion across most documentation criteria) [. observed a mild decrease in compliance with documentation criteria in the three-month period post-intervention, from 79 to 72%.Few studies described the durability of surgical ward round quality improvement strategies , 27, 28.Surgical rounding is an important aspect of perioperative care, with deficiencies in ward round communication and documentation associated with poorer patient outcomes , 38, 44.Few studies measured the impact of ward round interventions on patient morbidity and mortality. However, implementation of ward round checklists led to significant reductions in prescribing errors and critical errors related to the management of postoperative complications , 3. It wSubjective improvements in staff and patient communication were demonstrated with the use of checklists or proformas during the surgical ward round , 32, 34.Only three studies assessed longitudinal outcomes of their ward round interventions over time , 27, 28.Timing and efficiency of the surgical ward round is another consideration, with some staff apprehension about the extra time required to complete quality improvement interventions . HoweverThere are several limitations to this review. Data were derived from single-centre studies, with short follow-up durations and infrequent reporting of clinically relevant patient outcomes . The predominance of observational studies (~ 90% of studies) also introduces considerable selection and confounding bias, limiting the reliability of our conclusions. Most studies also used non-validated questionnaires to measure staff and patient satisfaction. The heterogeneity in outcomes and reporting of data between studies made it difficult to perform meaningful quantitative analyses. In addition, potential impacts of the Hawthorne effect  on outcomes was not accounted for in any of the studies , which cFuture research into the impact of different perioperative interventions should focus on larger patient cohorts, longitudinal follow-up of results, and objectively assessing for improvements in clinical outcomes via audit. The clinical and organisational framework for an optimal ward round are also important considerations, with key aspects being communication, early detection of complications, resilience to staff changes, staff well-being, efficiency, and regular auditing of ward round practices.Different types of ward round interventions have been implemented to improve the quality of patient care during the perioperative period. Use of checklists or proformas, telerounding, mobile tablet use, electronic patient records, a ‘surgeon of the week’ ward rounding system, as well as the introduction of active breaks during ward rounds have been associated with improvements in ward round documentation, communication, and satisfaction among staff and patients. Future studies should specifically investigate whether these different interventions are feasible to maintain in the long term, and their impact on clinically relevant outcomes such as patient morbidity and mortality.Supplementary file1 (PDF 672 KB)Below is the link to the electronic supplementary material.


Post-decision confidence from witnesses who make a positive identification decision can serve as a valuable indicator of identification accuracy under certain conditions. International best-practice guidelines therefore recommend asking witnesses to indicate their confidence following a selection from a lineup. Three experiments that used Dutch identification protocols, however, reported no significant post-decision confidence-accuracy association. To examine this conflict between the international and the Dutch literature, we tested the strength of the post-decision confidence-accuracy relationship for lineups that followed Dutch protocol in two ways: we conducted an experiment and re-analyzed two experiments that implemented Dutch lineup protocols. As expected, the post-decision confidence-accuracy relationship was strong for positive identifications and weak for negative identification decisions in our experiment. The re-analysis of the pre-existing data showed a strong effect for positive identification decisions of participants up to the age of 40 years. For exploratory purposes, we also tested the confidence-accuracy relationship between lineup administrators’ perception of witnesses’ confidence and eyewitness identification accuracy. In our experiment, the relationship was strong for choosers and weak for nonchoosers. The re-analysis of pre-existing data showed no correlation between confidence and accuracy, unless we excluded adults over 40 of age. We recommend adapting the Dutch identification guidelines to reflect the current and previous findings on the post-decision confidence-accuracy relationship. Eyewitness identifications can be valuable in the investigation of crimes, can serve as evidence in court, and can be instrumental in a conviction. Yet, overturned convictions all over the world have shown that it is possible for a sincere witness to identify the police suspect as the perpetrator and be wrong –5. Such A rich body of research shows that post-decision confidence from witnesses who make a positive identification decision can serve as a valuable indicator of identification accuracy e.g., –10). For. For10])choosers), but not for witnesses who reject the lineup . Secondhoosers; . Third, hoosers; . To ensuhoosers; . This mehoosers; ,17 and thoosers; , but seehoosers; ).According to Dutch identification guidelines , lineup The discrepancy between Dutch studies that find a weak confidence-accuracy relationship and the vast literature that finds a strong relationship could stem from differences in methodology, analysis, or both. Specifically, this relationship can be altered by procedural differences in lineup construction or administration or misinterpreted based on choices in statistical analyses. To explore the apparently weak confidence-accuracy relationship in Dutch experiments, we review both the methodology of Dutch identification procedures and the statistical analyses used to interpret the results.The Dutch Handbook for identification procedures  and to explain why. If a mock-witness selects the suspect, fillers are replaced to improve the similarity between the suspect and the fillers. Selected fillers are removed or replaced, as long as the resulting final lineup size is still six or larger. This procedure deviates from the mock-witness paradigm that researchers often use to test the fairness of a lineup . In thisA second important feature of Dutch lineups is that they are double-blind . For thiThree experiments that examined the confidence-accuracy relationship with the protocols and materials specific to the Netherlands did not report a significant association between post-decision confidence and identification accuracy. In all of the three experiments, participants witnessed a staged event and then viewed an 8 or 9-person target-present or target-absent lineup –25. The N = 337), post-decision confidence was not a statistically significant predictor of identification accuracy when entered with age, gender, level of education, and lineup administrator confidence in three separate regression models for live, photo, and video lineups [N = 104), a 2 (identification accuracy: accurate vs. inaccurate) x 5 (post-decision confidence level: 1 through 5) chi-square test did not return a significant effect [N = 109), post-decision confidence ratings between 1 and 5 were classified as low and post-decision confidence ratings between 6 and 7 as high. A 2 (accuracy: accurate vs. inaccurate) x 2 (confidence level: high vs. low) chi-square test returned no significant effect [In the first experiment . Most cIn addition to witnesses’ confidence judgements, the Dutch experiments also collected confidence judgments from lineup administrators. Such judgements served as a test of blind observers’ capacity to assess identification accuracy. The results were mixed. In two of the experiments, decisions judged “reliable” (confidence ratings 6 or 7), were more often accurate than “less reliable” decisions . Theperceived eyewitness accuracy differed as a function of the observer. Eyewitnesses’ confidence ratings were good predictors of their own accuracy—when their identification decision was accompanied by any justification except for familiarity. Familiarity justifications were associated with overconfidence errors. However, lineup administrators correctly predicted witness accuracy only when witnesses provided an unobservable justification for their choice. Lineup administrators interpreted other types of justifications more variably, therefore leading to inconsistent results. This finding suggests that justifications that accompany witnesses’ identification decisions can influence the diagnosticity of witness and lineup administrator confidence ratings.A recent North American experiment examined how the ability of witnesses and lineup administrators to assess identification accuracy varied as a function of witness justifications . WitnessOne possible explanation for the divergent findings of the Dutch experiments on the predictive value of lineup administrator confidence includes the involvement of very experienced lineup administrators  vs. lessWe tested the confidence-accuracy relationship in lineups that followed Dutch protocol in two ways: we conducted an experiment that followed the Dutch lineup procedure and we re-analyzed data from two earlier experiments that used Dutch identification protocols  took part in the experiment. They learned about the experiment through the University’s participant recruitment platform (SONA), online advertisements, flyers at various university faculties, and during open days at the university in which prospective students tour the faculty. The experiment was advertised as a study where participants would find out what it was like to be a police witness. The advertisement did not mention eyewitness identification specifically.N = 293 participants, . Participants were students (75.8%), had completed higher vocational education (11.9%), university education (7.5%), medium vocational education (3.1), or lower vocational education (1.7%). Participants received either 0.5 participation credits or a 5 € gift voucher. Data of 259 participants could be used for the association between lineup administrators’ confidence and witnesses’ identification accuracy. The smaller sample size for these analyses is due to the following: when testing during the open days, we had to involve two lineup administrators who were familiar with the hypothesis on lineup administrators’ confidence judgments. Therefore, we did not include those administrators’ confidence data. This experiment was approved by the standing ethical committee of the Faculty Psychology and Neuroscience at Maastricht University (code OZL_231_140_12_2020).We excluded 12 participants because they differed in origin from the targets in the stimulus films in order to prevent other-group bias as much as possible . We furtThe experiment used a one factorial design with two levels: target presence (target-present vs. target-absent). The position of the target in the lineup varied between positions 2, 5, and 6  and positions 3, 6, and 8 . We counterbalanced the roles of the two actors .Participants viewed one of two stimulus films that depicted a nonviolent mock-theft of a wallet. The script of the two films was identical, with the two actors switching roles (thief vs. victim) between versions. The action can be described as follows: person 1 (who will become the thief) is standing at a bus stop when person 2 approaches the bus stop on crutches. Person 2 takes a seat, takes something out of their backpack and leaves the backpack behind when they walk to the bus stop information board. At this time, person 1 opens the backpack, takes out a wallet, unnoticed by person 1, and walks away. The films were 1:24 and 1:37 min long and the perpetrator’s face was visible in close-up for approximately 19 sec.The target-absent and target-present lineups consisted of one suspect (innocent or guilty) and seven fillers. Lineup photographs showed each lineup member from the shoulders up in front of a white background; including one portrait picture and a 45° angle profile picture for each lineup member. The two pictures of each lineup member were presented simultaneously; the different lineup members were presented sequentially.We constructed the lineups in line with Dutch police protocol . For eaceffective lineup size by means of the mock-witness paradigm [ns between 30 and 38) who had not seen the stimulus event, read a target description similar to the descriptions used for the Dutch police protocol described above and viewed the lineup. Mock-witnesses then selected the person from the lineup who matched the description best. Tredoux’s E  ranged from E = 5.8 to 7.3 (of a possible 8), thereby marking them as a fair selection [We established the paradigm . Indepenelection ,36.We administered the lineups in line with Dutch police protocol . Prior tThe lineup was presented sequentially, using PowerPoint. Following Dutch procedure, each lineup member appeared on screen for 4 seconds followed by 3 seconds of a black screen. Lineup members were numbered 1–8. Lineups could only be viewed once and could be paused on the black screen. Participants first viewed two test photographs with the same timing as the actual lineup photographs. As a reminder, participants were then again warned that it was possible that the person wanted was not part of the selection and that they should not point out anyone if they were in doubt. These instructions were simultaneously given both orally and in writing, as part of the PowerPoint .If participants made a selection, the lineup administrator wrote down the number of the photograph. In line with Dutch procedure, the lineup then continued until the last lineup member. If the witness selected someone, lineup administrators covertly recorded their confidence in the participant’s lineup decision on a scale from 0 to 100% and then asked the participant how confident they were about the decision they had just made. If the participant did not select someone, administrators documented their confidence in the lineup rejection at the end of the lineup, just before asking participants for their confidence.The blinding of administrators was accomplished in two ways. The lineup was presented on two screens–one for the participant and one for the lineup administrator. The administrator’s screen was covered during lineup presentation such that only the number of the presented lineup member was visible (1–8). Additionally, and to avoid lineup administrators learning the likely position of the target over time, suspect positions varied between three different positions for each target and across film versions. Lineups were created by an experiment supervisor who was the only person aware of the suspect positions and counterbalancing scheme and who provided the lineup as a blinded file.All participants were tested individually and the identification procedure was video recorded. After signing the written informed consent form, participants viewed the stimulus film. During the 10-minute retention interval, participants provided demographic information and completed several unrelated filler tasks on the computer. Then, the lineup was administered. Afterwards, participants were asked what they thought the purpose of the experiment was prior to participating. In case the participant mentioned identification, they were asked to indicate the reason for their assumption. Participants furthermore indicated whether they knew any of the lineup members. A ‘Yes’ answer led to the exclusion of their data. Before leaving, participants were asked not to talk about the contents of the experiment to avoid bias in future participants. Participants then received a full debriefing and reimbursement.Seventy-one participants selected a lineup member from the lineup, 222 participants rejected the lineup. The correct identification rate for target-present lineups was 55/142 . The false selection rate in target-absent lineups was 13/151 . Considering that only one of the eight lineup members would be the innocent suspect, the false identification rate was 8.6/8 = .011%. The three remaining selections were foil selections in target-present lineups, and hence known errors. Descriptive statistics of confidence can be found in Witnesses’ confidence ratings: Correlations between post-decision confidence and identification accuracy. For choosers (n = 71), the smallest effect size we had 80% power to detect was r = .28 for a one-tailed test . For nonchoosers (n = 222), the smallest effect size we had 80% power to detect was r = .17. The confidence-accuracy correlation for choosers was r(69) = .41, p < .001, indicating a large effect. For nonchoosers, the effect size was small, r(220) = .19, p = .004.Witnesses’ confidence ratings: calibration analyses and calibration curves for witness confidence. n = 9; nonchoosers: n = 76), 70 and 80% , and 90 and 100%  to account for the small number of participants per confidence category [category –40.NRI = .119 signified a moderate capacity for differentiating between accurate and inaccurate chooser decisions.Choosers’ calibration curve followed the diagonal neatly, with slight overconfidence in the high confidence category, perfect calibration in the mid category, and some overconfidence in the lower part of the scale. Choosers’ overall calibration was very high at .0022. Over-/underconfidence across confidence categories was .032, indicating a general tendency for some overconfidence. The resolution index C = .0194 and the overall O/U = .082. The resolution index NRI = .026 indicated a weak capacity to differentiate between accurate and inaccurate nonchooser decisions. The upper part of C, O/U, and NRI with jackknife SEs  and 95%Posterior-by-prior Bayesian curves: Considering Base Rates of Target-Presence. In the current experiment, half of the lineups presented an innocent suspect instead of the true perpetrator. This means that the base rate, or prior probability that the suspect was the perpetrator, was 50%. In the final stage of the analyses, we addressed the possible concern that the confidence-accuracy relationship may hold only for certain base rates that the suspect is the perpetrator. To this end, we created posterior-by-prior Bayesian curves that map the probability that a suspect identification was accurate  across all possible base rate values from 0%  to 100% . Three important observations can be made from Lineup Administrators’ Confidence Ratings: Correlations. Lineup administrators’ post-identification confidence correlated positively with witnesses’ identification accuracy for choosers, r(58) = .30, p = .020, indicating a moderate effect size. For nonchoosers, the effect was weak, r(197) = .18, p = .011.r(21) = .25, p = .246, r(30) = .32, p = .075; nonchoosers: r(86) = .15, p = .16, r(94) = .19, p = .063, respectively. The sample is N = 239 here, because we only included data of experimenters who collected data prior to and following the training.We also wanted to study whether informing lineup administrators about non-verbal accuracy cues could be used as a potential tool for enhancing their ability to discriminate between accurate and inaccurate eyewitnesses. Midway through data collection, lineup administrators underwent a 15-min briefing. During this briefing, they learnt about the decision time-accuracy relationship ,45, absoLineup Administrators’ Confidence Ratings: Calibration Analyses. n = 17; nonchoosers: n = 57), 70 and 80% , and 90 and 100% .C = .0167. Over-/underconfidence across confidence categories indicated a general tendency for some underconfidence of lineup administrators, O/U = -.057. The resolution index NRI = .081 was indicative of moderate capacity of discrimination between accurate and inaccurate chooser decisions.Lineup administrators’ calibration curve for choosers looked quite similar to the witnesses’ curve in the high confidence and mid categories, with almost perfect calibration in the high confidence category and slight overconfidence in the mid category. In the low confidence category, lineup administrators showed strong underconfidence. Calibration for choosers’ decisions overall was good at C = .0269 and the overall O/U = .128. The resolution index was NRI = .037, indicating a weak capacity to differentiate between accurate and inaccurate nonchooser decisions. The lower part of C, O/U, NRI with jackknife SEs, and 95% CIs.Similar to witnesses’ confidence judgements, the curve was much flatter for nonchoosers, displaying strong overconfidence in the high confidence category, some overconfidence in the mid category, and perfect calibration in the low confidence category. Nonchoosers’ overall calibration was For both experiments ,25, we cN = 105, 61 women, age range = 17–67, Mage = 40.7, Mdnage = 45) were assigned to a context reinstatement (n = 55) or no context reinstatement condition (n = 50). Overall, the confidence-accuracy correlation was weak for both choosers and nonchoosers (see Participants (sers see . To testr(50) = -.06, p = .667, and nonchoosers, r(51) = -.03, p = .819, respectively.Lineup administrators’ confidence ratings and participants’ identification accuracy were not or only weakly correlated for both choosers, N = 105, 61 women, age range = 17–86, Mage = 40.7, Mdnage = 45). The findings were quite similar to Eigenhuis and van Amelsvoort [n = 12). There was not enough power to compare the correlation coefficients on an inferential level. Again and as expected, nonchoosers’ confidence-accuracy correlation was weak, regardless of participant age, with the exception of nonchoosers older than 60 years. The sample size of this group was again very small (n = 13).Participants were assigned to two groups, based on their age  = .36, p = .017, indicating a large effect size. The correlation coefficient was very small for nonchoosers, r(38) = -.08, p = 618.The correlation between lineup administrators’ confidence ratings and participants’ identification was strong for choosers, We tested the strength of the post-decision confidence-accuracy relationship for lineups that followed Dutch protocol in two ways: we conducted a new experiment and re-analyzed two experiments that used Dutch lineup protocols. As expected, the post-decision confidence-accuracy relationship for positive identifications in our experiment was strong. The re-analysis ,25 showeThe post-decision confidence-accuracy relationship was strong for positive identification decisions in this experiment that followed Dutch identification protocols . DiffereThe results of the re-analyses of two experiments that used Dutch lineup procedure were in line with the findings of our experiment, but the overall result pattern was complex. Specifically, age affected the post-decision confidence-accuracy correlation for positive decisions in both experiments ,25. The For exploratory purposes and to test the usefulness of a common Dutch procedure, we also analyzed the confidence-accuracy relationship between lineup administrators’ confidence and eyewitnesses’ decision accuracy. We tested this in the new experiment and re-analyzed existing data of two experiments that used Dutch lineup protocols. In our experiment, this relationship was strong for positive identifications and weak for negative identifications. In fact, the calibration and CAC curves for witnesses and lineup administrators looked strikingly similar, with the exception of the lowest confidence category. The re-analysis of the two experiments that used Dutch lineup procedure supported what the authors had originally reported, that is, a large effect size for Thijssen and van Amelsvoort  and a wen = 9). While impractical for statistical analyses, a low number of (very) low post-decision confidence selections is highly desirable for preventing innocent suspect identifications and might be a result of the elaborate warning instructions witnesses receive in the Netherlands. Importantly, there was still a wide variety in participants’ post-decision confidence judgments and a sufficient number of participants who displayed moderate and high post-decision confidence.This work has several limitations. Due to the COVID-19 pandemic, the collected sample of choosers was smaller than initially intended. This could have an effect on the stability of the calibration curve if we collapsed the data across five confidence categories as we initially planned. To address this issue, we followed previous research ,40 and cOverall, choosers’ identification accuracy in the current experiment was quite high at 77.5%. For a comparison, the average accuracy for lineup performance as reported in meta-analyses revolves around 50% –51. AddiSome specifics of the Dutch procedure could explain the relatively strong performance observed. In line with Dutch guidelines, participants received two warnings that they should only make a selection if they were sure enough, one prior to and one during the lineup. The warning during the lineup included written and oral instructions. The inclusion of the oral instructions in the Dutch procedure is based on one experiment that showed that oral instructions reduced false selections from target-absent lineups under certain conditions . The benThe re-analysis of the existing Dutch experiments was limited by the small sample sizes and the confounding of context reinstatement and age in one of the experiments . As a reOur results provide evidence that post-decision confidence can serve as a meaningful indicator of the accuracy of positive identification decisions for lineups following Dutch protocol. We recommend adapting the relevant Dutch guidelines on police procedures to reflect these findings. Specifically, we recommend that the guidelines implement instructions to collect immediate post-decision confidence judgments from adult witnesses who make a positive identification decision. Post-decision confidence provided by children or for negative identification decisions is not a meaningful indicator of the accuracy of the decision. Adult witnesses should provide a confidence judgment immediately following the identification decision and without any feedback provided to them. It goes without saying that, in the future, the Dutch (and any other) guidelines should take into consideration new insights about the confidence-accuracy relationship, for example about the impact of age and estimator variables ,19,52,53one piece of information when assessing a lineup decision. It is important to note that considering the post-decision confidence judgement does not alter the role of established impact factors of lineup performance . ConsidS1 Fig(TIF)Click here for additional data file.S1 File(DOCX)Click here for additional data file.