Consensus Statement on Injury Definitions and Data Collection Procedures in Studies of Football (Soccer) Injuries
Bibliographic record
Abstract
BACKGROUND: Variations in definitions and methodologies have created differences in the results and conclusions obtained from studies of football (soccer) injuries; this has made interstudy comparisons difficult. PROCEDURE: An Injury Consensus Group was established under the auspices of Fédération Internationale de Football Association Medical Assessment and Research Centre. Using a nominal group consensus model approach, a working document that identified the key issues related to definitions, methodology, and implementation was discussed by members of the group during a 2-day meeting. After this meeting, iterative draft statements were prepared and circulated to the members of the group for comment before the final consensus statement was produced. RESULTS: Definitions of injury, recurrent injury, severity, and training and match exposures in football, together with criteria for classifying injuries in terms of location, type, diagnosis, and causation are proposed. Proforma for recording players' baseline information, injuries, and training and match exposures are presented. Recommendations are made on how the incidence of match and training injuries should be reported and a checklist of issues and information that should be included in published reports of studies of football injuries is presented. CONCLUSIONS: The definitions and methodology proposed in the consensus statement will ensure that consistent and comparable results will be obtained from studies of football injuries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.623 | 0.604 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.018 | 0.012 |
| Research integrity | 0.017 | 0.028 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".