Standardising health history and injury surveillance of participants in endurance events: a modified Delphi consensus statement from the AMSSM runner health consortium
Bibliographic record
Abstract
Endurance events are popular worldwide and have many health benefits. However, runners and Para athletes may sustain musculoskeletal injuries or experience other health consequences from endurance events. The American Medical Society for Sports Medicine (AMSSM) Runner Health Consortium aimed to generate consensus-based survey items for use in prospective research to identify risk factors for injuries in runners and Para athletes training and competing in endurance events. The study design employed a modified Delphi approach, with a panel comprising 28 experts, including healthcare professionals, coaches, and athletes. Potential survey items were generated by panel members who subsequently engaged in three rounds of voting using Research Electronic Data Capture. Items were graded by clarity, relevance, and importance. Items achieving 80% consensus on all three aspects were retained. The response rate was 100% in R round 1 and 96% in Rrounds 2 and 3. Of 124 initial survey items, consensus was reached on 53, 34 and 22 items during Rrounds 1, 2, and 3, respectively. Two accepted items were removed due to redundancy. Combined with 10 non-voting items, 117 items covered key domains, including training and injury history, dietary behaviours and associated factors (such as menstrual function), footwear, mental health, and specific considerations for Para athletes. The consensus-based survey items should be considered by researchers to better understand the health of runners and Para athletes who train and compete in endurance sports to identify risk factors for injury.
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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 | 0.278 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".