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 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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".