MétaCan
Menu
Back to cohort
Record W4220846492 · doi:10.1136/bjsports-2021-104737

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’

2022· review· en· W4220846492 on OpenAlexaff
Wayne Derman, Marelise Badenhorst, Maaike M. Eken, Josu Ezeiza-Gomez, Jane Fitzpatrick, Maree Gleeson, Lovemore Kunorozva, Katja Mjøsund, Margo Mountjoy, Nicola Sewry, Martin Schwellnus

Bibliographic record

VenueBritish Journal of Sports Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsMcMaster University
Fundersnot available
KeywordsIncidence (geometry)MedicineAthletesMeta-analysisMEDLINESubgroup analysisPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.026
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.038
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0260.055
Bibliometrics0.0100.008
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.069
GPT teacher head0.351
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations17
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueBritish Journal of Sports MedicineSame topicExercise and Physiological ResponsesFrench-language works237,207