Risk factors associated with acute respiratory illnesses in athletes: a systematic review by a subgroup of the IOC consensus on ‘acute respiratory illness in the athlete’
Bibliographic record
Abstract
OBJECTIVE: To review risk factors associated with acute respiratory illness (ARill) in athletes, including non-infectious ARill and suspected or confirmed acute respiratory infections (ARinf). DESIGN: Systematic review. 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 were searched for prospective and retrospective full text studies that reported quantitative data on risk factors associated with ARill/ARinf in athletes, at any level of performance (elite/non-elite), aged 15-65 years. RESULTS: 48 studies (n=19 390 athletes) were included in the study. Risk factors associated with ARill/ARinf were: increased training monotony, endurance training programmes, lack of tapering, training during winter or at altitude, international travel and vitamin D deficits. Low tear-(SIgA) and salivary-(IgA) were immune biomarkers associated with ARill/ARinf. CONCLUSIONS: Modifiable training and environmental risk factors could be considered by sports coaches and athletes to reduce the risk of ARill/ARinf. Clinicians working with athletes can consider assessing and treating specific nutritional deficiencies such as vitamin D. More research regarding the role and clinical application of measuring immune biomarkers in athletes at high risk of ARill/ARinf is warranted. PROSPERO REGISTRATION NUMBER: CRD42020160928.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".