Football-specific extension of the IOC consensus statement: methods for recording and reporting of epidemiological data on injury and illness in sport 2020
Bibliographic record
Abstract
Several sports have published consensus statements on methods and reporting of epidemiological studies concerning injuries and illnesses with football (soccer) producing one of the first guidelines. This football-specific consensus statement was published in 2006 and required an update to align with scientific developments in the field. The International Olympic Committee (IOC) recently released a sports-generic consensus statement outlining methods for recording and reporting epidemiological data on injury and illness in sport and encouraged the development of sport-specific extensions.The Fédération Internationale de Football Association Medical Scientific Advisory Board established a panel of 16 football medicine and/or science experts, two players and one coach. With a foundation in the IOC consensus statement, the panel performed literature reviews on each included subtopic and performed two rounds of voting prior to and during a 2-day consensus meeting. The panel agreed on 40 of 75 pre-meeting and 21 of 44 meeting voting statements, respectively. The methodology and definitions presented in this comprehensive football-specific extension should ensure more consistent study designs, data collection procedures and use of nomenclature in future epidemiological studies of football injuries and illnesses regardless of setting. It should facilitate comparisons across studies and pooling of data.
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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 | 0.430 | 0.466 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.034 | 0.023 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.011 | 0.015 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.011 |
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".