Incidence of acute respiratory illnesses in athletes: a systematic review and meta-analysis by a subgroup of the IOC consensus on ‘acute respiratory illness in the athlete’
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence of acute respiratory illness (ARill) in athletes and by method of diagnosis, anatomical classification, ages, levels of performance and seasons. DESIGN: Systematic review and meta-analysis. DATA SOURCES: Electronic databases: PubMed-Medline, EbscoHost and Web of Science. ELIGIBILITY CRITERIA: Original research articles published between January 1990 and July 2020 in English reporting the incidence of ARill in athletes, at any level of performance (elite/non-elite), aged 15-65 years. RESULTS: Across all 124 studies (n=1 28 360 athletes), the incidence of ARill, estimated by dividing the number of cases by the total number of athlete days, was 4.7 (95% CI 3.9 to 5.7) per 1000 athlete days. In studies reporting acute respiratory infections (ARinf; suspected and confirmed) the incidence was 4.9 (95% CI 4.0 to 6.0), which was similar in studies reporting undiagnosed ARill (3.7; 95% CI 2.1 to 6.7). Incidences of 5.9 (95% CI 4.8 to 7.2) and 2.8 (95% CI 1.8 to 4.5) were found for studies reporting upper ARinf and general ARinf (upper or lower), respectively. The incidence of ARinf was similar across the different methods to diagnose ARinf. A higher incidence of ARinf was found in non-elite (8.7; 95% CI 6.1 to 12.5) vs elite athletes (4.2; 95% CI 3.3 to 5.3). SUMMARY/CONCLUSIONS: These findings suggest: (1) the incidence of ARill equates to approximately 4.7 per athlete per year; (2) the incidence of upper ARinf was significantly higher than general (upper/lower) ARinf; (3) elite athletes have a lower incidence of ARinf than non-elite athletes; (4) if pathogen identification is not available, physicians can confidently use validated questionnaires and checklists to screen athletes for suspected ARinf. For future studies, we recommend that a clear diagnosis of ARill is reported. PROSPERO REGISTRATION NUMBER: CRD42020160472.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.055 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".