Determination of future prevention strategies in elite track and field: analysis of Daegu 2011 IAAF Championships injuries and illnesses surveillance
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence and characteristics of newly incurred injuries and illnesses during international Athletics Championships, by improving the medical surveillance coverage, in order to determine future prevention strategies. DESIGN: Prospective recording of newly occurred injuries and illnesses. SETTING: 13th International Association of Athletics Federations World Championships in Athletics 2011 in Daegu, Korea. PARTICIPANTS: National team and Local Organising Committee physicians; and 1851 registered athletes. MAIN OUTCOME MEASURES: Incidence and characteristics of newly incurred injuries and illnesses. RESULTS: 82% of athletes were covered by medical teams participating with a response rate of 94%. A total of 249 injuries were reported, representing an incidence of 134.5 injuries per 1000 registered athletes, and 119 (48%) resulted in time loss from sport. A total of 185 injuries affected the lower limb (74%). Hamstring strain was the main diagnosis and 67% resulted in absence from sport. Overuse (n=148; 59%) was the predominant cause. A total of 126 illnesses were reported, signifying an incidence of 68.1 per 1000 registered athletes. Upper respiratory tract infection was the most common reported diagnosis (18%), followed by exercise-induced dehydration (12%), and gastroenteritis/diarrhoea (10%). The highest incidences of injuries were found in combined events and middle and long-distance events, and of illness in race walking events. CONCLUSION: During elite Athletics World Championships, 135 injuries, 60 time-loss injuries and 68 illnesses per 1000 registered athletes were reported. Higher risks of injuries were found in combined events and long-distance runs. Preventive interventions should focus on overuse injuries and hamstring strains, decreasing the risk of transmission of infectious diseases, appropriate event scheduling and heat acclimatisation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".