Injury and illness definitions and data collection procedures for use in epidemiological studies in Athletics (track and field): Consensus statement
Bibliographic record
Abstract
BACKGROUND: Movement towards sport safety in Athletics through the introduction of preventive strategies requires consensus on definitions and methods for reporting epidemiological data in the various populations of athletes. OBJECTIVE: To define health-related incidents (injuries and illnesses) that should be recorded in epidemiological studies in Athletics, and the criteria for recording their nature, cause and severity, as well as standards for data collection and analysis procedures. METHODS: A 1-day meeting of 14 experts from eight countries representing a range of Athletics stakeholders and sport science researchers was facilitated. Definitions of injuries and illnesses, study design and data collection for epidemiological studies in Athletics were discussed during the meeting. Two members of the group produced a draft statement after this meeting, and distributed to the group members for their input. A revision was prepared, and the procedure was repeated to finalise the consensus statement. RESULTS: Definitions of injuries and illnesses and categories for recording of their nature, cause and severity were provided. Essential baseline information was listed. Guidelines on the recording of exposure data during competition and training and the calculation of prevalence and incidences were given. Finally, methodological guidance for consistent recording and reporting on injury and illness in athletics was described. CONCLUSIONS: This consensus statement provides definitions and methodological guidance for epidemiological studies in Athletics. Consistent use of the definitions and methodological guidance would lead to more reliable and comparable evidence.
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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 | 0.479 | 0.483 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.015 | 0.011 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".