A Community-Based Model for Medical Management of a Large Scale Sporting Event
Bibliographic record
Abstract
OBJECTIVE: To develop and assess a community-based model for medical coverage for a large multisport event. DESIGN: The model included pre-event risk stratification, a concise training program for all medical volunteers, and detailed medical control guidelines. Prospective data collection was performed using standardized injury reporting forms. SETTING: The 2005 World Masters Games in Edmonton, Alberta, Canada. PATIENTS: Approximately 21,600 athletes between the ages of 25 and 97 who were participants in the World Masters Games. INTERVENTIONS: A 4-category risk scale was developed and applied to each sport. Medical volunteers were provided intensive training and guided by concise medical control guidelines. Medical encounters were recorded using a standardized injury report form. MAIN OUTCOME MEASURES: Incidence of injury by sport. Rate of ambulance transportation. Rate of medication use. Relevance of medical control guidelines. RESULTS: Medical coverage for over 80 venues was provided by 243 volunteers. A total of 1767 medical encounters were documented, with an overall injury rate of 8.2% (95% CI, 7.9 to 8.5). The majority of injuries were of a minor nature. Only 35 (0.16%) athletes had injuries that required medication or ambulance transportation. Cardiopulmonary resuscitation and defibrillation was required in only 1 patient. CONCLUSIONS: The risk of injury during the World Masters Games appears to be low, and the risk of severe injury is extremely low. The described community-based model for medical coverage for multi-sport events appears to be safe and practical.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".