Determinants of lung function changes in athletic swimmers. A review
Bibliographic record
Abstract
AIM: To summarise lung function characteristics of athletic swimmers and discuss mechanisms explaining these changes while putting forward the lack of a clear understanding of the precise physiological factors implicated. METHODS: Literature search until 07.2021 on Medline and EMBASE using keywords swimming, athletes, respiratory physiology, lung development, lung function tests. Relevant articles in French and English were reviewed. RESULTS: We found insufficient data to perform a meta-analysis. However, there is evidence that swimmers have better expiratory flows and increased baseline lung volumes than non-athletes or non-swimmers. Although these features can result from changes in lung development following intense training over the years, the contribution of a genetic predisposition and positive selection cannot be totally excluded. CONCLUSION: Disentangling the participation of constitutional factors and years of hard training to explain the larger lung volumes of athletic swimmers is in favour of an adaptative response of the lungs to early swim training through modification of the pathway of lung development. There seems to be an optimal window of opportunity before the end of growth for these adaptational changes to occur. Precise mechanisms, and contribution of adaptative change on lung physiology, remain to be further studied.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".