Air quality, respiratory health and performance in athletes: a summary of the IOC consensus subgroup narrative review on ‘Acute Respiratory Illness in Athletes’
Bibliographic record
Abstract
With the WHO stating that nearly 99% of the global population is exposed to air pollution levels that increase the risk of chronic diseases, the question of exercising in polluted environments is relevant to the health of athletes. Major sporting events held under conditions of poor air quality (AQ) have highlighted the lack of answers to concerns raised by organisers and athletes about the associated health risks. This evidence-based narrative review compiles current knowledge and identifies gaps regarding the relationship between AQ and sport. It is a summary of a more comprehensive report prepared for the International Olympic Committee (IOC) Medical and Scientific Commission. This article discusses the various sources of air pollutants encountered during exercise, summarises current AQ guidelines and provides insights into AQ conditions during the Paris 2024 Olympic and Paralympic Games (OPG) as well as in Los Angeles over the past four summers, in preparation for the 2028 OPG. It also summarises the effects of air pollution on the respiratory health and performance of athletes, while proposing mitigation strategies, with a particular emphasis on AQ education.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".