Injury and illness definitions and data collection procedures for use in epidemiological studies in Athletics (track and field): Consensus statement
Why this work is in the frame
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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.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it