427 Applying bayesian networks to injury occurrence in professional football
Bibliographic record
Abstract
<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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".