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 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 | 0.018 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".