427 Applying bayesian networks to injury occurrence in professional football
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Notice bibliographique
Résumé
<h3>Background</h3> Bayesian networks (BN) are directed acyclic graphs derived from empirical data that describe the dependency and probability structure. It may facilitate understanding of complex epidemiology by presenting the data in a multi-dimensional visual representation, and guiding inferences on the likelihood of the severity based on new information. <h3>Objective</h3> To provide a brief overview of BN and demonstrate its utility on a practical example of making inferences on days of absence when hypothetically new information was introduced. <h3>Design</h3> Retrospective analysis of prospectively collected injury data. <h3>Participants</h3> All male football players who were playing in the highest German professional league (Bundesliga) from 2014/15 to 2019/20 seasons were included. Players were identified from a publicly available database. <h3>Data analysis</h3> A BN structure was inferred using GeNIe 2.0. A search and score algorithm and existing empirical evidence knowledge were used to identify the structure. The variables included were age, height, weight, main position, part of the season, event, injury type, the injured body part, days of absence. The parameters were calculated with the expectation-maximization algorithm. <h3>Main Outcome Measurements</h3> Injury severity based on days of absence (mild: <4, minimal >4–7, moderate >7–28, severe >28). <h3>Results</h3> 3,030 player seasons were registered over the six seasons (age: 25.5±4.0, height (cm): 183.3±6.4 and weight (kg): 78.3±6.8), with 5,883 time-loss injuries. A network structure with distribution probability was built. A hypothetical scenario is used to illustrate how a BN makes inferences regarding injury severity. Case 1, a defender, 20 years old, suffered from a groin muscle injury. Case 2, a defender, 27 years old, suffered from a thigh muscle injury. Based on the BN constructed, we can infer the likelihood of the injury severity and the result is shown in Table 1. The result is based on the Bundesliga dataset and is specific to the study population. Counterfactual analysis may be used to inform coaches and clinicians about the likelihood of severity of an injury based on the features of the injury, for example, the characteristics of the player and the game. <h3>Conclusions</h3> The BN may offer an enhanced insight into the complex epidemiological systems and guide inferences on injury severity based on new information. This may potentially help clinicians in creating hypothetical scenarios on the severity and facilitate shared decision making.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle