Effect of a new concussion substitute rule on medical assessment of head collision events in Premier League football
Bibliographic record
Abstract
OBJECTIVE: To investigate the utilisation of additional permanent concussion substitutes (APCSs) and its efficacy with regards to rate and duration of medical assessment of head collision events (HCEs) in the 2020-2021 Premier League season. The present APCS rule allows players with a suspected concussion to be removed from a match without counting towards a team's allocated substitutions. METHODS: Eighty Premier League matches, 40 prior to additional permanent concussion substitutes implementation (Pre-APCS) and 40 after (Post-APCS), were randomly selected and analysed by a team of trained reviewers for HCEs. Data on HCE incidence, rates of medical assessment, duration of medical assessment and return to play were collected for each match. Data for the Pre-APCS and Post-APCS groups were compared to analyse differences in assessment of HCEs. RESULTS: During the 2020-2021 Premier League season, three APCSs were used. There were 38 HCEs identified in the Pre-APCS group (0.95 per match, 28.79 per 1000 athlete-hours of exposure) and 42 in the Post-APCS group (1.05 per match, 31.82 per 1000 athlete-hours of exposure). Incidence of HCEs (p=0.657), rates of medical assessment (23.7% Pre-APCS vs 21.4% Post-APCS; p=0.545) and duration of medical assessment (median 81 s Pre-APCS vs 102 s Post-APCS; p=0.466) did not significantly differ between the two groups. CONCLUSIONS: The implementation of APCSs in the Premier League did not impact the rate or duration of medical assessement of HCEs. Despite the introduction of APCSs, the consensus protocols for HCE assessment were rarely followed. We recommend changes to APCS and its implementation that would be aimed at protecting player health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".