Medical assessment of potential concussion in elite football: video analysis of the 2016 UEFA European championship
Bibliographic record
Abstract
OBJECTIVE: The objective is to determine if suspected concussions in elite football are medically assessed according to the International Conferences on Concussion in Sport consensus statement recommendations. SETTING: Men's Union of European Football Association (UEFA) Football Championship. PARTICIPANTS: All professional football players in the UEFA 2016 Championship Tournament. DESIGN: Observational study. OUTCOME MEASURES: Potential concussive events (PCEs) were defined as direct head collision incidents resulting in the athlete being unable to immediately resume play following impact. PCEs identified and description of PCE assessment and outcome were accomplished through direct standardised observation of video footage by trained observers in 51 games played in the Men's UEFA European Championship (10 June-10 July 2016). RESULTS: Sixty-nine total PCEs (1.35 per match) were identified in 51 games played during the 2016 Men's UEFA European Championship. Forty-eight PCEs (69.6%) resulted in two observable signs of concussion, 13 (18.8%) resulted in three signs and 1 (1.4%) resulted in four signs in the injured athletes. Nineteen (27.5%) PCEs were medically assessed by sideline healthcare personnel while 50 (72.5%) were not. Of the 50 PCEs that were not medically assessed, 44 (88%) PCEs resulted in two or more signs of concussion among injured athletes. Of the 19 medically assessed PCEs, 8 resulted in 3 signs of concussion, and 1 resulted in 4 signs; all assessments concluded in the same-game return for the injured athletes. CONCLUSIONS: PCEs were frequent events in the 2016 UEFA Euro championship, but were rarely assessed concordant with the International Conferences on Concussion in Sport consensus statement recommendations. There is an imperative need to improve the assessment and management of players suspected of concussion in elite football.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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 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".