Factors associated with illness in athletes participating in the London 2012 Paralympic Games: a prospective cohort study involving 49 910 athlete-days
Bibliographic record
Abstract
BACKGROUND: The incidence and factors associated with illness in Paralympic athletes have not been documented. AIM: To determine the factors associated with illness in athletes participating in the London 2012 Paralympic Games. METHODS: A cohort of 3565 athletes from 160 of the 164 participating countries in the London 2012 Paralympic Games were followed over a 14-day period (precompetition period=3 days, competition period=11 days; 49 910 athlete-days). Daily illness data were obtained from (1) teams with their own medical support who completed a daily illness log (78 teams, 3329 athletes) on a novel web-based system and (2) teams without their own medical support through the local organising committee database (82 teams, 236 athletes). Illness information from all athletes included age, gender, type of sport and the main system affected. MAIN OUTCOME MEASUREMENT: Incidence rate (IR) of illness (illness per 1000 athlete-days) and factors associated with IR (time period, gender, age and sport). RESULTS: The IR of illness was 13.2 (95% CI 12.2 to 14.2). The highest IR of illness was in the respiratory system, followed by the skin, digestive, nervous and genitourinary systems. The IR in the precompetition period was similar to that in the competition period, but the IR was significantly higher in athletics compared with other sports. Age and gender were not independent predictors of illness. CONCLUSIONS: Illness is common in Paralympic athletes and the main factor associated with higher IR of illness was the type of sport (athletics).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".