Frequency showed that mainly “Defense in Line of progression” and “Proximal contact” were the most activated variables, followed by “Harassment” and “Deterrence” for all constraints. However, only constraint 2 highly activated two collective motor behaviors, while the rest only did it with individual motor behaviors. It is concluded that the constraints used in training seem to be effective in activating a group of defensive handball motor behaviors, specifically those that are basic for female school handball players.Traditional models to train and teach young players in team sports assume that athletes learn as linear systems. However, an actual methodology called Non-Linear Pedagogy (NLP) accounts for the fact that the players and the team are complex dynamic systems. Experiences in handball under this methodology are scarce; due to this, an observational study has been conducted with a follow-up, idiographic and multidimensional design, in which 14 female school handball players belonging to four different local teams in Santiago, Chile (age = 15.55 + 0.51) agreed to participate in three special handball training sessions with the use of the NLP methodology where three different constraints were used. Descriptive analysis with the Chi-squared test showed a total of 252 observations where most of the variables were dependent on the constraints ( Handball, as a team sport, requires the domain of tactical knowledge, coordinative skills, physical fitness, mental strength, motor skills, social relationships, leadership, communicative skills, power, endurance, speed and decision-making abilities, among several other variables ,2. All oHowever, traditional teaching models for team sports have only focused on the development of technique, followed by tactical skills and the introduction of the rules of the game, using an isolated context complemented with strength and conditioning training . Once thIn this sense, traditional training models have pushed the nature of teams sports as a non-cyclical game to a standardized one, where the elements of uncertainty have decreased, improving the dependence on strength and conditioning work with a lack of creativity from the players, except for those exceptional highly talented ones .The aforementioned is possible to see in handball, as it has been stated that performance indicators in the World Handball Championship for men and women are exclusively the “Anthropometric Indicators” (only for men), “Age index,” and “Scoring index,” while tactical behaviors, decision-making, and team players interaction are not taken into account . Also, iIn response to these limitations, an alternative teaching model has been developed based on the complex dynamic system theory called Non-Liner Pedagogy (NLP), which involves the interaction between the players and the environment through the implementation of adapted games , comprehThe NLP allows players to elaborate paths of verbal and non-verbal communication, organize strategical responses, reproduce emergent tactical motor behaviors, analyze the environment to make decisions, and especially adapt their individual resources to improve the team’s performance . The NLPThe NLP to teach team sports was first described by the study of Chow et al.  as an arWith regards to the CMs and their effects on motor behavior, one of the first studies that have described an experimental design is the study of Praxedes et al. . Their rLater, specifically in handball, only a few studies were conducted using the NLP ,17,18,19Limited evidence has been reported according to handball’s defensive motor behaviors developed by NLP. At the time this article was made, only the study of Flores–Rodríguez and Ramírez–Macías  has giveAs the evidence has demonstrated, the effectiveness of NLP on handball´s attacking motor behaviosrs is still limited, and due to the lack of more evidence regarding handball’s defensive motor behavior and the absence of this kind of study in female players, researchers have hypothesized that the use of NLP in handball training might activate specific handball’s defensive behaviors. Therefore, is the aim of this research to describe the effects of the use of NLP methodology in the development of defensive motor behaviors in young female handball players.This is a descriptive, quantitative, observational study planned to obtain relevant information regarding our investigation objective. Particularly, this corresponds to a follow-up, idiographic and multidimensional design, as there was an inter and intra-sessional analysis which provided data about the frequency of the performed actions and about some behaviors of the players independently as part of a system (team) in both individual and collective responses . The samBefore the study started, both the parents/guardians and the participants were notified and informed about the procedures, making it clear that their participation was totally voluntary. Also, informed consent was signed by the parents/guardians as well as by the players. It should be noted that this research was approved by the Research Ethics Committee of the University of Granada under the registration number: 2000/CEIH/2021, responsibly ascribing to the standards agreed in the Helsinki agreement [(A)Warm-up: Where all players were asked to jog for 5 min, then to practice freely and individually, for 5 more minutes, all the different abilities they knew, and later in couples, to jog freely, passing the ball between them;(B)Individual defensive technical training: Consisted in different exercises related to defensive movements, specifically to move oriented according to their opponent, to maintain distance, or to apply pressure depending on the attacker’s intention;(C)NLP exercises: A specific exercise is applied in every session, however, with different constraints in each 1;(D)Offensive training: Comprised of specific exercises to improve mobility and dynamism during the ball circulation performed by the attackers. The goal was always to keep the speed of the ball, passing it among players while they were moving side to side by performing different combinations of movements with no opponents;(E)Cool-down: In all sessions, players were asked to apply self-massage and stretching.All the selected players participated in 3 special handball training sessions that comprised the same structure described next:All participants were asked not to eat food and not to ingest stimulant beverages such as coffee or energy drinks at least an hour before training sessions. Also, players were asked to maintain the same type of diet that they present daily. For hydration, the players did its ad-libitum. At the time players participated in the study training sessions, no training on their clubs was performed, thus not competing either.As seen in previous studies regarding NLP methodology ,23,24, a-Defenders must prevent attackers from moving forward to the goal area;-Defenders have individual responsibilities, although they can help their teammates without losing their individual opponents;-Defenders must prevent attackers from penetrating the space between and behind the defenders.In each session, before the exercise started, the coach explained out loud to the defenders the next: These 3 indications correspond to what Anton (2003) has described as the fundamental pillars of defensive tasks.-Session 1: Each attack of the 6 had to be done with the 6 balls at the same time, and no circulation of an attacker could be done through the central circle.-Session 2: Each attack of the 6 had to be done with only one ball at a time, and no circulation of an attacker could be done through the central circle.-Session 3: Each attack of the 6 had to be done with only one ball at a time, and the circulation of an attacker can be done through the central circle as a pivot position for only 3 s.For each session, a constraint was given to conditioning the motor behavior of defenders. These constraints were the next:The coach remembered out loud the constraints for the DT players, and no other information was given before or during the exercise, as well as no feedback after it. It is important to highlight that none of the players had experience with collective defensive tactics before this study.® (USA) added to a tripod which was located on the side of the court 4 m high [® (USA), in which the NLP exercise of every session was saved independently of the rest of the session. Every NLP video was at the time edited to highlight every time the exercise started or finished and also when the role of the teams changed; this was to facilitate posterior analysis using the Kinovea® software (Open Source). For the observation, an ad hoc observation instrument described by Anguera et al. [As this is an observational study, every session was video recorded using the main integrated camera of an iPhone XR 64GB4 m high . Every ra et al.  was adapa et al.  : Every time the player should perform the action, but she didn´t; EP (Effectively Performed): Every time the player performed a correct action as the situation need it; NN (Non-Necessary): Every time a defensive behavior was not necessary to be performed. ® spreadsheet using a numeric code for each qualification  to determine the final amount of observation correctly performed.Finally, all the results were arranged in an Excel (USA)® v28  was used for the data analysis. As this is an ideo-graphic, follow-up, and multidimensional observational study, descriptive statistics are presented as frequency. To compare the frequency of the emergent or non-emergent handball´s defensive behaviors regarding the applied constraints, a contingency table was used. To determine if emergent or non-emergent handball motor behaviors are dependent on the applied constraints, the chi-square test was used.The statistical software SPSSp-value < 0.001 was obtained except for “Proximal Contact,” in which the results showed a p-value of <0.005, and for “Defensive Adjustment Through the Back” where the values remain all the same for each observation, so this variable was taken as a constant, so the frequency was not given. In this sense, it is evident that almost all variables are effectively dependent on the constraints. The descriptive analysis  followedWhen constraint 1 was applied , all the handball’s specific defensive motor behaviors got activated. However, not all of them were at the same frequency. Mainly, two individual behaviors, which are “Defense in line of progression” and “Proximal contact,” were activated the most, with an average of 57 observed actions. For the rest of the handball’s specific defensive motor behaviors, an average of 10 observed actions were obtained .Regarding constraint 2 , “Defense in line of progression” and the “Proximal contact” were highly activated again with the same average of 57 observed actions. Nonetheless, group defensive motor behaviors were also activated in a higher frequency, with 57 and 52 observed actions for “Timely Helps” and Defensive Titl” correspondingly. All the remaining handball’s specific defensive motor behaviors were activated as well but at a lower frequency. However, compared to constraint 1, a higher number of actions were seen for “Harassment”, with 39 more actions in constraint 2 compared with constraint 1. In the same way for “Deterrence”, 24 actions were detected above the four seen in constraint one. A similar phenomenon occurred for “Defensive Adjustment”, “Ball Interception”, and “Change of Opposition”, where an average of 14 observed actions got activated above the seven actions seen during the first constraint .On the other hand, the third constraint  also activated the “Defense in Line of Progression” and “Proximal Contact” in this opportunity with a higher number of observed actions (79 and 74 correspondingly). Differences are possible to observe when comparing constraint three with constraints one and two, as the group defensive motor behaviors reduced their frequency to 16 for “Timely Helps” and 0 for “Defensive Tilt”. Another difference is that “Harassment”, “Deterrence”, and “Defensive Adjustment” were activated more during the third constraint compared with the second and the first. These handball’s defensive motor behaviors presented an average frequency of 23 observed actions above the same behaviors during constraint 2 and an average of 46 above constraint one .Finally, constraint 2 was the one which has activated more collective actions such as “Timely helps”, “Defensive Tilt”, “Change of Oppositions”, “Deterrence”, and “Defensive Adjustment” compared with constraints 1 and 3. However, constraint 3 has activated in a higher frequency two of the aforementioned behaviors while constraint 1 only activated a few behaviors in a lower amount.The aim of the present study was to describe the effects of the use of NLP methodology in the development of defensive motor behaviors in young female handball players. For this, three constraints were introduced to a specific exercise which was repeated in three training sessions (one constraint per session) following the examples of the studies of Flores-Rodríguez & Ramírez-Macías .It has been stated that it is essential for handball coaches to be aware of the influence that constraints exert on the players’ behaviors . As mentIn handball, to defend against any offensive action, there are several technics and tactical skills which respond to three basic pillars that always have to be accomplished during the whole game: to avoid attackers from getting near their zone of effectiveness, to avoid attackers from shooting at the goal, and to recover the ball at any given opportunity ,31,32. TRegarding the motor behaviors of “Harassment” and “Deterrence,” these were slightly activated during constraint one. This may have happened due to the lack of receivers of a pass, as all attackers had a ball in their possession at the beginning of the exercise. In addition, the limitations that attackers in possession of the ball have in terms of mobility throughout the court compared with defenders due to the rules of the game  probablyParticularly constraint two was the only one that activated in a high frequency of actions the two collective motor behaviors, “Timely helps” and “Defensive Tilt”. As mentioned before, this constraint allowed more attackers to move unlimitedly through the court, with the exception of the central zone, prioritizing movements in width over those in-depth toward it. In general, the attackers tend to seek free space to penetrate into the central zone, as often is done in the real game in young female teams ,38. The In addition, the study of Musa & Menezes  about coThe handball’s motor behavior of “Ball Interception”, “Change of Oppositions”, and “Ball Interception during Dribbling” were all slightly activated in all three constraints. The explanation for this is that in most of the attacks in every session, dribbling was barely used, passes were often given far away from the defense, and crosses of the attacker were not given regularly, except for the constrain two that activate “Change of Opposition” 21 times. However, these crosses were not significant in reaching the goal.One of the main limitations of our research has been the difficulty of limiting the order effect that sessions may have had on the results. However, as this is an observational study, the results are a description of what has been observed. Also, a second difficulty was finding data regarding technical and tactical skills in young female handball players, as most of the studies refer to strength and conditioning training or anthropometric profiles. However, there was enough literature from well-known international handball coaches that helped the present discussion.After the analysis of the results and the discussion of them, the authors agree that the constraints used in the proposed exercise give the impression of being effective in activating a group of specific defensive handball motor behaviors for young female handball players. It is important to highlight that also, by analyzing the three constraints separately, these activate differently, so the exercise used in this research with the correspondent constraint could also be used independently to improve a specific group of defensive motor behaviors.


Reduced muscle mass (RMM) is a phenotypic criterion for malnutrition; the appendicular skeletal muscle mass index (ASMI) and fat-free mass index (FFMI) are both applicable indicators in the global leadership initiative on malnutrition (GLIM) guideline. However, their sensitivity and prognostic effect remain unclear.2 for men and < 5.7 kg/m2 for women) or FFMI (< 17 kg/m2 for men and < 15 kg/m2 for women). Propensity score match analysis and logistic regression analysis were used to evaluate the efficacy of FFMI and ASMI in diagnosing severe malnutrition and multivariate Cox regression analysis to determine the efficacy of RMM in predicting survival.Clinical data of 2,477 patients with malignant tumors were collected. Multi-frequency bioelectrical impedance analysis was used to obtain ASMI and FFMI. RMM was confirmed by ASMI (< 7.0 kg/mP < 0.05). A 1:1 matched dataset constructed by propensity score match contained 810 cases. RMM.FFMI was an influential factor of severe malnutrition with HR = 3.033 , and RMM.ASMI was a predictive factor of overall survival .In total, 546 (22.0%) and 659 (26.6%) participants were diagnosed with RMM by ASMI (RMM.ASMI group) and FFMI (RMM.FFMI group); 375 cases overlapped. Body mass index (BMI), midarm circumference, triceps skinfold thickness, and maximum calf circumference were all significantly larger in the RMM.FFMI group for both sexes (In general, RMM indicates negative clinical outcomes; when defined by FFMI, it predicts nutritional status, and when defined by ASMI, it is related to poor survival in cancer patients. In 2018, the number of cancer patients and cancer-related deaths increased by 18.1 million and 9.6 million globally, respectively .Cancer-related malnutrition results in changes in body composition, mainly muscle depletion, and deteriorating biological function. A decreasing food intake or absorption and inflammation are the main reasons –7. In faCancer-related malnutrition is related to frequent use of antibiotics and long hospitalization, resulting in decreased quality of life and increased cost and psychological pressure . AccordiThe study protocol adhered to the Declaration of Helsinki and was approved by the Ethics Committee of the First Hospital of Jilin University (2017-362).The clinical data of patients with malignant tumors admitted to the First Affiliated Hospital of Jilin University from November 2011 to December 2018 were collected. Inclusion criteria were as follows: (1) age > 18 years old and (2) pathological diagnosis of malignant tumors. Exclusion criteria were as follows: (1) ≥2 coexisting types of tumors; (2) suffering from server pleural effusion and/or ascites; (3) under regular hemodialysis; or (4) death within 3 days after admission.®). Both ASM and FFM were recorded. (6) Survival data: OS was recorded from diagnosis to mortality due to any cause. The corresponding formulas used are as follows:Clinical data for each participant were collected by trained personnel. Laboratory examinations, anthropometric measurements, and bioelectric impedance analysis (BIA) were completed within 3 days of admission. Operating details are displayed in 2 for men and < 5.7 kg/m2 for women) or FFMI (< 17 kg/m2 for men and < 15 kg/m2 for women) as measured by BIA.GLIM recommends measurement by dual-energy absorptiometry or other validated body composition measures including BIA for detecting reduced muscle mass (RMM). In this study, RMM was confirmed based on ASMI  and R version 4.0 .t-test was used for normally distributed data. Counting data were analyzed using the chi-square test, and the z-test with Bonferroni adjustment was adopted for multiple comparisons. Next, propensity score match (PSM) analysis was performed. Multicollinearity was tested by linear regression analysis; a variance inflation factor (VIF) >10 was considered to indicate collinearity. Conditional logistic regression analysis was adopted to evaluate the efficacy of FFMI and ASMI in diagnosing severe malnutrition (PG-SGA ≥ 9). The shared frailty model for survival analysis was then performed to determine the efficacy of RMM in predicting survival benefit. A P-value of < 0.05 was considered to indicate statistical significance.A Venn plot was drawn to depict the overlap and division of RMM diagnosed by FFMI and ASMI. The Kolmogorov–Smirnov test was used to confirm normal distributions of continuous data. An independent P < 0.001). Age, smoking history, drinking history, comorbidities, tumor sites, and metastasis did not differ between the groups (P > 0.05)  and 659 (26.6%) participants were diagnosed with RMM by ASMI (RMM.ASMI group) or FFMI (RMM.FFMI group), respectively. There was an overlap of 375 cases, comprising 68.7% of the RMM.ASMI group and 56.9% of the RMM.FFMI group . In tota > 0.05) .P > 0.05). Then, the anthropometric measurements were compared after stratifying by sex. BMI, MAC, TSF, and CC were all significantly larger in the RMM.FFMI group for both sexes (P < 0.05) (P > 0.05).No significant difference was detected in albumin, CRP, leukocytes, neutrophils, lymphocytes, platelets, NLR, PLR, and SII between the groups ( < 0.05) . HGS didP > 0.05) (P = 0.122). However, RMM.FFMI was an influential factor of severe malnutrition even in the multivariate model, with HR = 4.070  after adjusting all involved characteristics  was matched considering severe malnutrition (PG-SGA ≥ 9) as a dependent variable. The matched dataset contained 810 cases; all basic characteristics were comparable ( > 0.05) . Leukocy > 0.05) . A condieristics .P = 0.025) in the univariate Cox regression analysis but not in the multivariate analysis . Apart from the baseline factors, albumin , neutrophils , and TSF  were considered as influential factors of OS  but RMM.FFMI did not in any subgroup (P > 0.05). Thus, Cox regression was performed in the subgroups to further clarify the role of RMM.ASMI in predicting OS  and platelets  were maintained in the Cox regression model. RMM.ASMI was not retained in both models.Next, two 1:1 matched subgroups were constructed, which were adjusted by all baseline factors  and took RMM.FFMI and RMM.ASMI as the dependent variables, respectively. The RMM.ASMI subgroup contained 1,034 cases and the RMM.FFMI subgroup contained 1,232 cases. Since the baseline characteristics were already matched, shared frailty survival analysis was performed in each subgroup. As shown in cting OS . In bothRMM, measured by FFMI and ASMI, was considered a phenotypic criterion for malnutrition in the GLIM guideline. The prevalence of RMM diagnosed by FFMI and ASMI was 22.0 and 26.2%, respectively, with approximately three-fifths overlapping. As shown by sensitive analysis, FFMI was significant in diagnosing severe malnutrition. However, in survival analysis, although the effect of ASMI was always significant in univariate regression, neither FFMI nor ASMI was retained in multivariate regressions. These results are due to intrinsic differences between FFMI and ASMI and implied their discrimination in clinical utility, which should be paid attention.Chronic inflammation and depletion accompany cancer over the whole process . InfectiThe FFMI is calculated based on the FFM, which represents the body composition except fat including muscle and bone mass, and organs such as the liver. The ASMI, calculated based on the ASM, merely refers to the skeletal muscle mass in the limbs. Thus, the FFM is more consistent with the weight and BMI, as supported by the present results. PG-SGA is the gold standard of malnutrition assessment for cancer patients, based on weight loss, symptoms, activities and function, metabolic demand (largely refers to inflammation), and physical examinations. As a part of the latter, the muscle assessment includes parts of the torso such as the temples , clavicle , shoulders (deltoids), interosseous muscles, scapula , and a small part of the muscles in the thigh (quadriceps) and calf (gastrocnemius). Therefore, RMM as measured by FFMI closely relates to the severe malnutrition detected by PG-SGA ≥ 9. ASM can be persevered by physical activity, especially resistance exercises, as recommended by the guidelines in sarcopenia . A detecHowever, the decreased ASMI was not included in the multivariate regression model. Parameters such as platelets, neutrophils, TSF, and BMI were unstable in a sensitivity analysis. In contrast, albumin always contributed to nutritional status and survival. Gupta et al.  performeThere are some limitations to this study. First, selection bias might exist because this was a retrospective study and the demand for complete clinical data. Second, despite adopting PSM and sensitive analyses, the value of unstable parameters such as platelets, neutrophils, TSF, and BMI requires further analysis to provide a confidential reference.In conclusion, this study revealed that RMM indicates negative clinical outcomes and highlighted the intrinsic differences between FFM and ASM, suggesting the need for rational choice in clinical practices to support future decision-making in cancer patients. More importantly, RMM as defined by FFMI predicts nutritional status, whereas when defined by ASMI, it is related to poor survival in cancer patients. In addition, serum albumin appeared to be an influential factor for both malnutrition and survival. The instability of other parameters revealed by sensitive analysis reminds clinical practitioners of precious opinions on these parameters.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of the First Hospital of Jilin University. The patients/participants provided their written informed consent to participate in this study.All authors made a significant contribution to the study reported, whether that is in the conception, study design, execution, acquisition of data, analysis, and interpretation, or all these areas took part in drafting, revising, or critically reviewing the article gave final approval of the version to be published have agreed on the journal to which the article has been submitted and agreed to be accountable for all aspects of the study.


For individuals suffering from severe refractory ulcerative colitis (UC) who are unresponsive to medical treatment, a total proctocolectomy and ileal pouch-anal anastomosis (IPAA) surgery is the gold-standard treatment. However, its complications include anastomotic leaks, pelvic or perianal abscesses, and rare complications such as pouch volvulus. To our knowledge, there is a scarcity of case reports on patients with, specifically, a recurrent pouch volvulus. We present a case of a 57-year-old female with refractory UC who had undergone this treatment with no initial complications; 15 years later, she presented with intermittent bouts of obstruction. An exploratory laparotomy was performed; however, no adhesions or necrosis were found. Following investigations, pouch volvulus was confirmed. She subsequently underwent four endoscopic decompressions in the same year and ultimately received an enteropexy of the pouch. The volvulus reoccurred and, ultimately, the decision was made to perform a loop ileostomy. The patient, to date, is alive and doing well with her permanent ileostomy. An ileal pouch volvulus occurs when the pouch wraps around its mesenteric axis; if it is not promptly treated, obstruction and ischaemia may occur. Pouch volvulus is a rare complication of an ileal pouch-anal anastomosis (IPAA) surgery, occurring in roughly 0.18% of cases [The aim of this report was to address the issue as to why such volvuli reoccur after a total proctocolectomy and IPAA surgery and to determine the best course of their management.Clostridioides difficile colitis infection and cytomegalovirus (CMV) colitis; however, both were ruled out. Crohn’s disease was also considered, but the patient’s clinical picture was more suggestive of UC. The decision was made for her to undergo a total proctocolectomy and an IPAA surgery. The outcome of this surgery was successful with no further complications until 15 years later (2005) when the patient presented to a hospital in Galway complaining of intermittent bouts of obstruction. At that time, a decompression endoscopy was performed along with an exploratory laparotomy . Prior to her presentation, she had undergone intensive medical treatment for UC; however, she was referred by gastroenterology due to the failure to respond to this treatment. Upon presentation, her vital signs were normal, but she had abdominal distention, central abdominal pain (without guarding), absolute constipation last 24 hours), emesis, and was apyretic. There was no family history of any significance. The UC was debilitating in her everyday life as she had to pass stool five to six times daily. At the time, differential diagnoses included a 4 hours, A further decompression endoscopy was performed, which revealed a dusky mucosa and "spokes of a wheel" presentation, similar to the first decompression she had undergone Figure . The patFor individuals suffering from severe refractory UC who fail to respond to medical treatment, the gold-standard surgical treatment is a total proctocolectomy and IPAA surgery . In thisImaging, specifically CT, is a necessary component to diagnose pouch volvulus. CT scans can display evidence that is characteristic of volvulus, such as a whirl sign and air-fluid levels , as seenTo our knowledge, there is a dearth of case reports on patients with recurrent pouch volvulus after undergoing a total proctocolectomy and IPAA surgery. After an extensive PubMed search, we found only seven other cases that presented with recurrent pouch volvulus -16. In fAnother case study of note, by Perotti et al., performed electrocautery of the sacral hollow via laparoscopy to promote adhesion of the pouch . The patTraditionally, IPAA procedures were performed in an open fashion; however, with advances in medical technology, a minimally invasive approach using laparoscopy is now preferred, which has made recovery time quicker and the likelihood of adhesions lower ,18. AdheLastly, Itabashi et al.  suggesteWe propose several recommendations for the management of pouch volvulus in the future, which include conducting more research on the prevalence of volvulus and underlying causes as to why it occurs, including genetic and environmental predisposition, as well as determining the best course of treatment upon patient presentation. Despite the rarity of pouch volvulus after IPAA surgery, recurrent volvuli can be extremely debilitating in everyday life, and hence an approach that provides certainty in terms of success needs to be identified. Lastly, more research should be done on the effects of promoting adhesions within the sacral hollow as a natural way of stabilising the pouch. Because most papers report a "free-floating pouch", natural stabilisation through adhesions in the initial IPAA surgery may drastically reduce the likelihood of pouch volvulus. It is important to note that these are only recommendations for the future and actual developments may vary from our proposals. The management of pouch volvulus will continue to evolve as new research and technologies emerge.We presented a case of recurrent pouch volvulus. Pouch volvulus is a rare but serious condition that can lead to significant morbidity and mortality if left untreated. The recurrence of this phenomenon is extremely rare and poorly understood. However, prompt diagnosis and management are essential to prevent serious complications. Further research is needed to better understand the epidemiology and pathophysiology of pouch volvulus and its recurrence, in order to develop improved diagnostic and therapeutic strategies.


This paper introduces a new challenge and datasets to foster research toward designing systems that can understand medical videos and provide visual answers to natural language questions. We believe medical videos may provide the best possible answers to many first aid, medical emergency, and medical education questions. Toward this, we created the MedVidCL and MedVidQA datasets and introduce the tasks of Medical Video Classification (MVC) and Medical Visual Answer Localization (MVAL), two tasks that focus on cross-modal  understanding. The proposed tasks and datasets have the potential to support the development of sophisticated downstream applications that can benefit the public and medical practitioners. Our datasets consist of 6,117 fine-grained annotated videos for the MVC task and 3,010 questions and answers timestamps from 899 videos for the MVAL task. These datasets have been verified and corrected by medical informatics experts. We have also benchmarked each task with the created MedVidCL and MedVidQA datasets and propose the multimodal learning methods that set competitive baselines for future research. In recent years there has been an increasing interest in video question-answering8 tasks, where given a video, the systems are expected to retrieve the answer to a natural language question about the content in the video. We argue that only predicting the natural language answer does not seem to reflect the real world, where people interact through natural language questions and expect to localize the moments from the videos to answer their questions. The majority of the existing work on video question answering (VQA) focuses on (a) open-domain applications by building the VQA datasets10 consisting of movies, TV shows, and games, and (b) retrieval9 of the natural language answers. With increasing interest in AI to support clinical decision-making and improve patient engagement11, there is a need to explore such challenges and develop efficient algorithms for medical language-video understanding.One of the key goals of artificial intelligence (AI) is developing a multimodal system that facilitates communication with the visual world  using a natural language query. In recent years, significant progress has been achieved due to the introduction of large-scale language-vision datasets and the development of efficient deep neural techniques that bridge the gap between language and visual understanding. Improvements have been made in numerous vision-and-language tasks, such as visual captioninghow to place a tourniquet in case of fingertip avulsions?” The textual answer to this question will be hard to understand and act upon without visual aid. To provide visual aid, we first need to identify the relevant video that is medical and instructional in nature. Once we find a relevant video, it is often the case that the entire video can not be considered as the answer to the given question. Instead, we want to refer to a particular temporal segment, or a sequence of moments, from the video, where the answer is being shown, or the explanation is being illustrated. The straightforward moment retrieval via an action, object, or attribute keyword may not uniquely identify the relevant temporal segment, which consists of the visual answer to the question. A more natural way to refer to the appropriate temporal segment  and Medical Video Question Answering (MedVidQA) datasets13 for medical instructional video classification and question answering. The MedVidCL dataset contains a collection of 6,617 videos annotated into ‘medical instructional’, ‘medical non-instructional’ and ‘non-medical’ classes. We adopted a two-step approach to construct the MedVidCL dataset. In the first step, we utilize the videos annotated by health informatics experts to train a machine learning model that predicts the given video to one of the three aforementioned classes. In the second step, we only use high-confidence videos and manually assess the model’s predicted video category, updating the category wherever needed. The MedVidQA dataset contains the collection of 3,010 manually created health-related questions and timestamps as visual answers to those questions from trusted video sources, such as accredited medical schools with an established reputation, health institutes, health education, and medical practitioners. We have provided a schematic overview of building the MedVidQA and MedVidCL datasets13 in Figs. Toward this, this work introduces the Extraction of Medical and Health-related Tasks from wikiHow: With an aim to obtain medical instructional videos that describe how to perform certain health-related activities, we first start by compiling an extensive list of health-related activities using wikiHow (https://www.wikihow.com/Main-Page) – an online resource that contains 235,877 articles on how to do a certain task for a variety of domains ranging from computer and electronics to philosophy and religion, structured in a hierarchy. We start with extracting the medical and health-related tasks from the wikiHow. We collected a total of 6,761 how-to tasks from the wikiHow ‘Health’ category.Identification of Relevant Health-related Tasks: In the second step of the dataset creation, we filter the compiled collection extracted from wikiHow. A medical informatics expert reviews each wikiHow task and marks them as relevant or non-relevant from a medical instructional perspective. We keep only those tasks for which the textual answer will be hard to understand and act upon without visual aid such as “how to relieve multiple myeloma pain” or “how to apply cold therapy”. This procedure yields 1,677 medical and health-related instructional tasks. We have provided the distribution of the selected instructional tasks category in Fig. Searching YouTube for The Relevant Videos: To acquire the relevant videos, we use the task name as a query to search YouTube via its Data API (https://developers.google.com/youtube/v3). In order to collect only the most relevant videos, we only keep the top 4 videos returned by YouTube. We deduplicate videos based on YouTube IDs because some videos may appear in multiple health-related instructional tasks. However, if a video was uploaded multiple times or edited and re-uploaded, the dataset may still contain duplicates.Expert Annotation for Medical Instructional Videos: Medical informatics experts further categorize the relevant medical instructional videos retrieved from YouTube searches. We perform this important step in our dataset creation because (1) videos retrieved by the YouTube search may not be instructional for particular medial queries, and (2) ensures the reliability of the medical videos. To identify the medical instructional videos in a pool of YouTube videos, we define medical instructional videos as follows: a medical instructional video should clearly demonstrate a medical procedure providing enough details to reproduce the procedure and achieve the desired results. The accompanying narrative should be to the point and should clearly describe the steps in the visual content. If a valid medical query is aligned with an instructional medical video, it should explain/answer the medical query with a demonstration, be a tutorial/educational video where someone  demonstrates a procedure related to the medical query, or be a how-to video about the medical query. Medical instructional videos may target different levels of expertise, ranging from good Samaritans providing first aid to medical students learning a procedure or experienced clinicians interested in continuing medical education. For this study, we focus on the instructional medical videos that do not require medical education, i.e., the instructions should be at a level that is understandable and can be performed by a layperson. For example, if a nurse shows how to bandage a wound in an emergency, the video is instructional for a layperson. Conversely, if a doctor explains how to perform a specific surgical procedure, the video is instructional for professionals but not for the general public.Formulating Instructional Question and Visual Answer from Videos: With the aim of formulating medical and health-related questions and localizing their visual answer in the videos, we start with the medical instructional videos annotated in the previous step of the dataset creation. A question is called instructional if the answer requires a step-by-step demonstration and description of the actions to be taken to achieve the goals. For many medical questions, the answer to the question is better shown than described in words, and the answer will be hard to understand and act upon without visual aid, e.g., “how to perform a physical examination for breast abnormalities?” Three medical informatics experts were asked to formulate the medical and health-related instructional questions by watching the given video and localizing the visual answer to those instructional questions by providing their timestamps in the video. We asked the annotators to create questions for which (1) answers are shown, or the explanation is illustrated in the video, (2) the given video is necessary to answer the question, and (3) the answer cannot be given as text or spoken information without visual aid.To create the MedVidQA dataset, we follow a systematic approach that involves the contributions of medical informatics experts at multiple stages. The detailed steps to build the dataset are as follows:(A)Collecting Medical and Non-medical Videos: With an aim to reduce the annotation efforts, we follow a two-step process to build the MedVidCL dataset. In the first step, we seek a high-confidence opinion on the video category from a pre-trained video classifier. In the second step, medical informatics experts validate the video category predicted by the video classifier. In order to train the video classifier, we begin with collecting medical and non-medical videos. We utilized the human-annotated 1,016 medical instructional and 2,685 medical non-instructional videos from MedVidQA dataset. To collect non-medical videos, we sampled 1,157 videos of non-medical categories  from the HowTo100M14 dataset, which is a large-scale YouTube video dataset with an emphasis on instructional videos. We perform a stratified split on this collection and used 80% videos for training, 10% for validation, and 10% for testing.(B)Building Video Classifier: We focus on only coarse-grained  categorization of the videos as opposed to the fine-grained  video classification15 where the micro-level human activity recognition is the key to correctly categorizing the video. Therefore, we hypothesized it is possible to predict the coarse-grained category from the natural language subtitles of the video. Towards this, we propose an ensemble classifier that aggregates the predictions of deep learning and statistical classifiers. We used the support vector machine (SVM)16 with TF-IDF features as the statistical classifier in our ensemble learning setup, and we chose the pre-trained BigBird17 model as our deep learning classifier as BigBird is capable of accommodating longer sequences that are ubiquitous in video subtitles. We utilized the Hugging Face’s implementation (https://huggingface.co/google/bigbird-roberta-large) of the BigBird model. After extracting the English video subtitles using the Pytube library (https://pypi.org/project/pytube/), we fine-tuned four different pre-trained BigBird models, each with 1024 as the maximum token length on our training dataset. We also used early stopping to prevent overfitting the model. Since our training dataset has a skewed distribution of the classes, we consider penalizing the model in the training phase for the misclassification made for the minority class by setting a higher class weight and, at the same time, reducing the weight for the majority class. For the class c ∈ C, we set the weight C is the set of all classes in the dataset and N is the total number of samples in the dataset. Nc is the number of samples associated with class c in the dataset. We follow the Population Based Training (PBT)18 mechanism to jointly optimize a population of models and their hyperparameters to maximize performance. PBT inherits the idea of exploitation and exploration from genetic algorithms. In PBT, each member of the population exploits - taking into account the performance of the whole population, a member can decide whether it should abandon the current solution and focus on a more promising one - and explores - considering the current solution and hyperparameters, it proposes new ones to better explore the solution space. Following this, we fine-tune the BigBird with the PBT strategy (population size = 5) and consider the top-2 performing members of the population as final models. We used two different PBT strategies to train the BigBird model. In one strategy, we penalize the model and call their top-2 members of the population as https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html) with the default hyperparameters settings to train the SVM classifier on our training dataset. We used majority voting from predictions of all five different (4 BigBird + SVM) models in our ensemble learning setting. We break the ties with predictions from the best-performing classifier. The detailed video classification results are shown in Table (C)Identification of Relevant Videos: We sampled a subset of videos from the large-scale HowTo100M and YouTube8M19 datasets, and we only chose medical and health-related videos from a set of predefined categories marked as appropriate by medical informatics experts. This process yields a collection of 66,312 videos from HowTo100M and YouTube8M datasets.(D)Predicting Relevant Video Categories from the Video Classifier: We utilized the ensemble classifier to predict the category of the relevant videos. The ensemble classifier predicted 13,659 medical instructional videos, 5,611 medical non-instructional videos, and 47,042 non-medical videos from the collection of 66,312 relevant videos.(E)Sampling High-Quality Videos and their Manual Assessment: In order to create a high-quality dataset, we only chose the videos for which the classifier confidence was high for a specific video category and filtered out the videos for which the ensemble classifier confidence was low. In the first step, we filtered out all the videos from the predicted medical-instructional category for which the classifier confidence was below 0.8. A similar strategy was used for medical non-instructional (confidence score below 0.9) and non-medical (confidence score below 0.99). The second and final step involved the manual assessment of the classifier-predicted videos.(F)Refining Medical Instructional Videos into the Fine-grained Category: To further advance the medical instructional category videos, two annotators annotated each video into the wikiHow medical categories . Toward this, we provided hierarchical labels for each medical instructional video, specifically coarse-grained and fine-grained labels. We have demonstrated the label hierarchy in Fig. A video question-answering system that can provide visual answers to medical or health-related instructional questions must have the capability to distinguish between medical instructional and non-instructional videos related to the user’s questions. Towards building systems to perform this task efficiently and effectively, we created the MedVidCL dataset, which can be used to train a system that can distinguish among the medical instructional, medical non-instructional, and non-medical videos. The details of the approach to build MedVidCL dataset are described as follows:13. The OSF link contains two directories, one for the MedVidCL dataset and the other for the MedVidQA dataset. The MedVidCL directory contains the training, validation, and test split of the MedVidCL dataset. The README file also contains detailed information about each split, including statistics and sample code to process the data and download the videos. Each dataset split is saved in JSON format, which lists data items. Each item in the JSON file contains the relevant key (metadata name) and value (metadata information) pairs, which are video_id, video_link, video_title and label. The detailed statistics of the MedVidCL dataset are shown in Table sample_id, question, answer_start, answer_end, video_length, video_id and video_url. The details of each key are provided in the README file. The statistics of the MedVidQA dataset are given in Table We have archived a total of nine data records with Open Science Framework (OSF), available at 10.17605/OSF.IO/PC594(a) accredited medical schools with established reputations, (b) health institutes, (c) health education, (d) hospitals, (e) medical professionals or experts discussing a particular health-related topic, (f) or medical professional appearances and discussions on news channels. We developed an annotation interface  amongst them.In the first step of the MedVidQA dataset creation, we aim to identify and use only trustworthy videos and YouTube channels. A video is categorized as a reliable video if it belongs to a YouTube channel from any of the following sources: ace Fig.  to selecace Fig.  amongst In the second step of MedVidQA creation, we focus on creating medical or health-related instructional questions. To ease the annotation task, we developed an annotation interface Fig.  to formuMedical Instructional’, ‘Medical Non-instructional’ and ‘Non-medical’) videos from the MedVidQA dataset. We considered this set as the training set for the MedVidCL dataset. To build a validation and test set, we sampled high-confidence videos predicted by a video classifier. To further validate the dataset, we asked the medical informatics expert to review the video category predicted by the model. The expert was asked to correct the video category if the video classifier mislabels it and mark the videos as non-relevant if there is no conclusive evidence in the videos to label them into any of the video classification categories. The final MedVidCL dataset contains 6,117 videos, amongst which 1,489 are medical instructional, 2,994 are medical non-instructional, and 1,634 are non-medical. We further removed those videos which have a duration longer than 20 minutes. We have provided the dataset’s detailed statistics in Table Musculoskeletal Health (35.12%)”, “First Aid (24.37%)”, “Equipement (13.63)” and “Pain (13.02%)”. Table To build the MedVidCL dataset, we chose human-annotated  and obtained the RGB visual features 24. We also extracted the word representation of the question 25. As done before21, we utilized the character embedding 26 to enrich the word representation and obtained the final question representation as 27. We call the video and question representation obtained from the feature encoder as 23 to capture the cross-modal interactions between video and question features.We benchmarked the MedVidQA dataset by performing a series of experiments using state-of-the-art natural language video localization approaches. We adopt the proposed architecture21. Particularly, it uses two unidirectional LSTMs - the first to predict the start timestamp and another to predict the end timestamp of the answer. The first LSTM, labeled LSTMs, takes the tth feature e, computes the hidden state U ∈ Rd2 (W ∈ Rd2) and b ∈ Rd2 (c ∈ Rd2) are the weight matrices and biases, respectively. Finally, the probability distributions of the start and end positions are computed by The attention flow mechanism provides the question-aware video feature representation Ps and Pe averaged over all samples in the batch. The network trained using the span prediction loss is called video span localization (VSL-BASE). We also experiment with the Query-Guided Highlighting (QGH) technique introduced prior21 and call this new network VSL-QGH.The network is trained by minimizing the sum of the negative log probabilities of the true start and end answer position by the predicted distributions α, a hyperparameter. An extended answer span aims to cover additional contexts and help the network focus on subtle differences between video frames. We use the 300 dimensional Glove embeddings and 50 dimensional character embeddings to obtain the word representation in both the VSL-BASE and VSL-QGH networks. We also use 1024 dimensional video features throughout the experiments and hidden state dimensions of 128 in both the LSTM and Transformer-based encoder. The VSL-BASE and VSL-QGH networks are trained using AdamW optimizer28 for 30 epochs with an initial learning rate of 0.0001. The best-performing models are chosen based on the performance (IoU = 0.7) on the validation dataset.With the QGH technique, the target answer span is considered as the foreground and the rest of the video as the background. It extends the span of the foreground to cover its preceding and following video frames. The extension is controlled by the extension ratio (a) Intersection over Union (IoU) that measures the proportion of overlap between the system predicted answer span and ground truth answer span, and (b) mIoU which is the average IoU over all testing samples. Following a prior protocol29, we have used “R@n, IoU = μ”, which denotes the percentage of questions for which, out of the top-n retrieved temporal segments, at least one predicted temporal segment intersects the ground truth temporal segment for longer than μ. In our experiment, we only retrieved one temporal segment; therefore, we have n = 1. Following previous studies29, we have reported μ ∈{0.3, 0.5, 0.7} to evaluate the performance of the VSL-BASE and VSL-QGH models.We have evaluated the results using c.f. Table We have performed extensive experiments  with a frame position length of 800. For IoU = 0.3, an FPL (Frame Position Length) value of 1400 seems to outperform other variants of the VSL-BASE model. With an optimal frame length of 800, we perform our next set of experiments with the VSL-QGH model.α, and the network is trained with join span prediction and visual region (foreground or background) prediction losses. We experimented with the VSL-QGH model by varying the α from 0.05 to 0.3 and reported the results in Fig. α = 0.25. We reported the result for the VSL-QGH model with its optimal value of α in Table The VSL-QGH models depend on the extension ratio https://pypi.org/project/pytube/). We then trained statistical classifiers such as Linear SVC30, and SVM16 to predict the video category by considering the language features. Specifically, we first removed common stop words (https://www.nltk.org/book/ch02.html#stopwords_index_term) from the video subtitle to make a clean subtitle text and then train both SVM variants by extracting the TF-IDF features from the clean subtitle text. We have also experimented with pre-trained language models such as BERT-Base-Uncased31, RoBERTa-Base32, and BigBird-Base17. For vision-based monomodal approaches, we extracted 20 frames from each video at a uniform time interval and used these frames as the sequence information to predict the video category. To process these frames, we utilized a 3D ConvNet (I3D), which was pre-trained on the Kinetics dataset24, and the resulting features were passed as input to both LSTM and Transformer networks to predict the video category. We have also experimented with the pre-trained (https://huggingface.co/google/vit-base-patch16-224) ViT33 model for extracting frames and obtained the frame representation from the ViT feature extractor. Similar to the I3D, we passed the resulting features to LSTM and Transformer networks to predict the video category. We extended our experiments from monomodal to multimodal settings by considering both the language and vision features. Using the language input (video subtitle) and vision input (video frames), we obtained their representations either from LSTM or Transformer networks. We then concatenated the language and vision features together and passed the concatenated features to a feed-forward layer with softmax activation to predict the video category. Similar to the monomodal (vision) experiments, we use both I3D and ViT features to perform the multimodal experiments. We also extend the aforementioned experiments to assess the model’s performance on coarse-grained medical instructional videos. Since the prediction of the coarse-grained medical instructional video belongs to the multilabel classification problem, therefore, we replace the softmax activation of the classification with sigmoid activation function to accommodate all the probable classes given the medical instructional video.We benchmarked our MedVidCL dataset with multiple monomodal and multimodal approaches. For monomodal approaches, we built several models by considering the language (video subtitle) and vision (video frames) separately for different models. To develop language-based monomodal approaches, we extracted the English subtitles from the videos using the Pytube library  experiments. Each pre-trained Transformer model was trained with the AdamW optimizer with the learning rate = 5e-5 for 10 epochs and with a batch size of 8 . The LSTM and Transformer hidden states are set to 128 and 1024, respectively, for all the monomodal (vision) and multimodal experiments. Each monomodal (vision) and the multimodal network was trained with the learning rate = 5e-5 for 30 epochs with a batch size of 16. We set the maximum text sequence length for multimodal experiments to 512. For the coarse-grained medical instructional video classification, we trained the mono-models and multimodal and determined the best checkpoint  based on the model’s performance (in terms of weighted F1-score) on the validation dataset.We chose the hyperparameters values based on the classifier performance (average macro F1-score) on the validation dataset. For the Linear SVC and SVM classifiers, the optimal regularization value (a) F1-score on Medical Instructional Class, and (b) macro average F1-score across all the classes. Following the prior work35 of multilabel image classification, we evaluated the performance of the system in predicting the coarse-grained medical instructional videos using the metrics mean average precision (mAP) and F1-score.The evaluation metric to assess the performance of the systems are We have provided the detailed results of multiple monomodal and multimodal approaches in Table BigBird-Base, vision monomodal: ViT + Transformer, and multimodal: L + V (ViT) + Transformer on the test set of the coarse-grained medical instructional videos of the MedVidCL dataset. We reported the results in Table ViT + Transformer. The detailed class-wise performance of each coarse-grained class is shown in Table Oral Health” category. “Musculoskeletal Health”, “ENT”, and “Brain & Nerves” are the next few labels where the BigBird-Base model reported considerable performance. With the variety of multiple approaches, our goal is not to establish a state-of-the-art approach for the task rather, we wanted to provide strong baselines and insights on the monomodal and multimodal approaches for the MedVidCL dataset. We believe that a sophisticated language-vision fusion mechanism will further improve the performance  of the multimodal approaches.We also experiment with the best language monomodal: 13 describing how to process the MedVidCL and MedVidQA datasets. The source code to process the video and extract the features for building models can be found in the GitHub repository (https://github.com/deepaknlp/MedVidQACL).We have provided detailed instructions in the README file of the Open Science Framework repository (10.17605/OSF.IO/PC594)The videos used to create the MedVidCL and MedVidQA datasets are publicly available on YouTube. We have provided the links to download them along with the other metadata information.https://bionlp.nlm.nih.gov/).For the MedVidQA and MedVidCL datasets, we have extracted the features using a 3D ConvNet-based I3D model that was pre-trained on the Kinetics dataset. Additionally, we have also extracted video features for the MedVidCL dataset using the pre-trained ViT model. We have publicly released these video features, which can be downloaded from our resource page (


Content was selected based on a realistic simulation scenario of the vaccine administration process to a patient-actor. Items with concordance greater than 0.8 and 0.6 were considered valid, verified using the Content Validity Index (CVI) and the Content Validity Ratio (CVR), respectively.judges’ CVI had an average of 97.5%, and CVR, 0.9, and health professionals’ CVI, 95.4%, and CVR, 0.8. Successes in administering vaccines were addressed, such as careful reading of labels, double-checking the vaccine, distractions/interruptions and error reporting.the video was constructed and validated in terms of content, and can be used in training professionals working in vaccination. However, safe vaccination is a worldwide concern and a determining factor for the success or failure of immunization programs-4. In recent decades, there has been a significant increase in the number of vaccines applied to children and adults and, consequently, the increase in the occurrence of immunization errors globally-6.Vaccination is a strategy capable of impacting the control or elimination of vaccine-preventable diseasesProgramas Nacionais de Imunizações),7-9.Immunization error (IE) is defined as any preventable event arising from the inappropriate use of immunobiological agents that may generate negative impacts, such as impaired immunization of a person, Events Supposedly Attributable to Vaccination or Immunization (ESAVI), in addition to increased costs for the health service and reduced confidence in Brazilian National Immunization Programs ; content ; relevance (characteristics of images and scenes proposed in the script); environment (scenario assessment); and verbal language (language used in the video). A five-point Likert scale was used, where judges should choose the option that corresponded to their opinion: Strongly Agree (SA); Agree (S); Neither agree/disagree (NN); Disagree (D); and Strongly Disagree (SD). A space was allocated for the personal interpretative analysis of a committee of judges.For the script/storyboard analysis with the committee of judges, a questionnaire validated in a previous study and adapted to the IE theme was usedThe next stage, video production, consisted of filming the scenes present in the validated script. The images and audio were recorded by the researchers, who had the help of a professional trained in journalism regarding the best angle to record, suitable lighting, correct positioning of the cameras and ways to improve the audio.Volunteer nursing professionals who work in vaccination rooms participated in the recording of scenes and testimonies. The choice of professionals was guided by their familiarity with the topic of vaccination and proximity to the researchers. After agreeing to participate in the recordings, professionals signed the Term of Authorization for the Use of Image and Voice. The script texts were sent to the professionals to be rehearsed prior to the recordings.The recordings were made in two vaccination rooms, where nursing professionals who participated in the video worked, and some scenes from the realistic simulation scenario were used in the video. The decision to film in the vaccination rooms was due to the need to reproduce reliable scenes that portrayed reality and also a concrete physical environment. And the use of two rooms was to facilitate the participation of volunteer professionals. Images of the rooms, simulation of vaccination practice, testimonials from professionals, photos and audiovisual effects were used..For the video presentation and narration, there was the participation of a professor who teaches vaccination and has experience in performing arts. In this phase, the presence of the researchers was essential to assess whether the video’s objective was being achievedIn the post-production phase, the video editing, finalization and final organization was carried out by a contracted company, specialist in video editing, and all editing and montage was done together with the researchers so that the video followed the validated script. Windows MovieMaker, version 16.4.3528.0331, FormatFactory 1.70 and PhotoScape, version 3.6.2 were used. Using this software, scenes were selected, edited and organized, the soundtrack was chosen and edited and visual effects were inserted, images were assembled and paired, characters, subtitles and figures were superimposed., applied to expert judges, also organized on a Likert-type scale with five response levels.After completing the post-production phase, the video was assessed by the target audience, consisting of 3 nurses and 9 invited nursing technicians. The professionals were approached at the health units to watch and assess the video on a date previously scheduled with the researcher. In the health units where each professional worked, the video was shown, and each one was asked to assess whether the language and images were clear and easy to understand or whether there was any suggestion. For assessment, the same instrument was usedItem suitability analysis was performed using the Content Validity Index (CVI). The calculation of this index was carried out by dividing the sum of responses considered suitable (SA and A) by the total number of responses. Items that obtained concordance rates greater than or equal to 80% (0.8) among judges and the target audience were considered valid.e - (N/2) / (N/2)], where “ne” is the number of judges and professionals who classified each item as “SA or A” and “N” is the total number of judges and professionals responding. The minimum CVR expected for this study was of 0.6 for judges and 0.5 for the target audience. After the assessments, suggestions were accepted and the final version of the video was defined based on suggestions made by judges and the target audience.The Content Validity Ratio . While CVI measures the proportion of judges and professionals with answers (SA+A), the CVR compares this proportion with the expected number, if judges/professionals were responding at random [ female, aged between 33 and 60 years. Most participants are graduates in nursing (92.3%), and most of them (46.3%) have a postdoctoral degree.There was unanimous agreement (100%) of judges regarding the following categories: environment and verbal language. In the objective category, item 1.4 showed 99% agreement. One of the judges justified that the video script covered much more than what was proposed in the research objectives, justifying that the video will contribute to the error reporting culture, increasing the study relevance.Zé Gotinha”’s expression (PNI mascot), used to illustrate a scene in the script. A judge suggested replacing “Zé Gotinha’s negative expression”, which suggests disapproval by IE, which is a form of moral punishment, with an expression of sadness, compassion on the part of patients for the damage that has occurred. Another judge recommended that, instead of just talking about the importance of placing a sign with the words, “In attendance, wait”, in the vaccination room, it would be interesting to film a professional doing this, encouraging this to be an action in every vaccination service.In the content category, in items 2.5 and 2.6, it was suggested to add a speech about the consequence of the error for professionals, such as negative feelings, which becomes the event’s second victim. Another suggestion was the modification of “In the relevance category, in item 3.3, a judge pointed out that the image of a child smiling, at the end of the video, could bring ambiguous interpretations to the vaccination process, that of the good of vaccination, for disease prevention, and that of the non-’real’ imaginary, since, normally, children cry when they are vaccinated.Reformulations of excerpts from the narration were suggested, with a view to making them more understandable by professionals. It was suggested to change the title of the video from “Immunization error: forms of prevention” to “Preventive strategies for immunization errors”. It was recommended to include information on the organization of immunobiological agents in the refrigerated chamber and their identification with labels, aiming at safety in the vaccination room. The suggested changes were accepted, and the necessary changes were made. Judges’ total CVI and CVR were 97.5% and 0.9, respectively.Video validity by target audience was carried out by 12 professionals who work in immunization rooms, 3 nurses (25%) and 9 nursing technicians (75%). All participants had worked in the immunization room for more than one year.In professionals’ assessment, the CVR and the total CVI for the categories were: objectives (96% and 0.7); content (95% and 0.6); relevance (95% and 0.8); environment (91% and 0.8); and verbal language (100% and 1.0). The video was considered relevant, with suitable and understandable language, reflecting the vaccination room’s reality. Two professionals reported that the sound could have been a little louder. Due to the fact that the recording took place during the COVID-19 pandemic, some nursing professionals wore a mask, which may have impaired the sound.The final version of educational video “Preventive strategies for immunization errors” ended with 12 minutes and 35 seconds..The simulation scenario was important to represent a safe vaccination and direct the debriefing to the discussion on IE prevention. Realistic simulation brings significant impacts on the teaching and learning structure, for being characterized as a strategy that replicates precisely an event, situation, environment or scenario, allowing the link between theory and practice, contributing to the meaningful learning of learning by doingThe methodological route, used in the educational video construction and validity, was fundamental for the development of a quality material. The script/storyboard construction, based on realistic simulation and debriefing as well as the use of scientific literature and the wide experience of professionals in the subject can justify the almost totality of judges’ agreement.. The judges, who validated the video, had direct experience with vaccination and, therefore, were able to contribute with relevant suggestions for improving the video. It is extremely important to validate the script for the educational video, since validity provides a sharper look from people with experience in the proposed topic, increasing the reach of the material being produced.Educational technology assessment by judges on the subject is important to avoid the construction of material with superficial or briefly exposed content, compromising the effectiveness of the technology-30. It is an accessible way of bringing information in an interactive and dynamic way, being able to educate, communicate and inspire the public as a non-linear form of teaching, different from traditional forms, being able to sensitize and catch viewers’ attention.Health education videos have been used as an alternative for teaching that provides a meaningful experience for participantsThus, it is considered that the almost nullity of disagreements between experts and the perception of good understanding and scope of the content of the video produced may be related to the methodological rigor covered in this study. The judges agreed with the forms of prevention present in script/storyboard, since they follow the vaccination process proposed by the Brazilian PNI, suggesting forms of prevention that can be carried out in vaccination practice, in addition to considering a clear and objective language..As for the content addressed in this video, it is understood that it is a difficult topic to discuss, due to the relationship with the idea of punishment and blaming professionals. IE are a reality faced by health systems and nursing, and measures for prevention, such as the adoption of protocols and checklists in vaccination rooms, investments in information technology, permanent education of professionals and detection of risk factors for the occurrence of errors, are fundamental. The contents addressed in the video point to several aspects of prevention, such as the correct administration of vaccines by professionals, such as careful reading of labels, double-checking the vaccine  and avoiding distractions and interruptions. These actions are opportunities to transform individual attitudes, in order to be protective in their uniqueness, and collective attitudes, for the entire team’s safety.IE as well as other errors are caused by the interaction of several factors related to the professional, the environment and the institution/organization.Another point addressed in the video was the importance of the institution providing enough products, inputs and human resources, as the responsibility for developing error prevention strategies is not exclusive to health professionals. The improvement of working conditions, such as a sufficient number of workers and suitable structure, guarantees quality patient care and professional safety-34. Studies point to the incompleteness of reporting forms,35-36, which causes a loss in case assessment, in addition to possible underreporting.To err is human, hence the importance of error reporting. IE reporting is an important trigger to expose problems and risks that must be analyzed for the implementation of systematic and strategic changes to improve safety and quality of care.The elaborated video reinforces the importance of error reporting, which can be the first attitude, in the sense of promoting users’ and professionals’ safety. It is important to encourage health professionals to reflect on their work in order to carry out actions that value their safetyAs a study limitation, the video validity with the target audience of only one municipality is pointed out, and there can be no generalization.Vaccination activities in the Brazilian public service are still specific to nursing professionals. The educational video construction and validity about IE prevention emerges as a technological resource that can be incorporated into nurses’ educational strategies, contributing to safety in the vaccination room.The educational video for IE prevention was created and considered valid by judges and professionals working in vaccination rooms.The forms of prevention described in the video addressed successes in administering vaccines, such as carefully reading labels and double-checking the vaccine . It was pointed out the risk of distractions and interruptions for error occurrence and the need for the health institution to offer products, inputs and sufficient human resources, in addition to reinforcing the importance of reporting for the identification of causes and improvements in quality of care in the vaccination room. Video is a technological resource that facilitates the teaching-learning process that can be used to train professionals working in vaccination rooms.