Effect of intense swimming training on rhinitis in high‐level competitive swimmers
Bibliographic record
Abstract
BACKGROUND: Rhinitis is commonly reported by swimmers. Seasonal allergic rhinitis may impair athletes' performance and quality of life (QOL). No data are currently available on the changes of nasal symptoms during and after a swimming season. We aimed to determine in competitive swimmers: (1) the prevalence of rhinitis and its impact on their QOL during an intense training programme, (2) the changes in nasal symptoms and QOL after a resting period and (3) the relationship between rhinitis and airway hyperresponsiveness (AHR). METHODS: Thirty-nine swimmers and 30 healthy controls answered the Rhinitis Quality of Life Questionnaire (RQLQ) and scored nasal symptoms on a seven-point Likert scale during the week preceding their visit. Subjects had allergy skin prick tests and a methacholine challenge. Peak nasal inspiratory flows were also measured. The athletes performed these tests during an intense training period (V1), outside the pollen season and after at least 2 weeks without swimming (V2). RESULTS: At V1, rhinitis symptoms were reported by 74% of swimmers and 40% of controls (P<0.01). Eighty-four percent of swimmers and 72% of controls were atopic (NS). RQLQ score was higher in swimmers compared with controls at V1 (27.3+/-28.5 vs. 9.5+/-12.7, respectively, P<0.005). The presence of AHR during training did not correlate with the presence of rhinitis symptoms. At V2, the nasal symptoms and RQLQ scores were similar in swimmers and controls. CONCLUSION: Intense swimming training is associated with an increase in nasal symptoms and impairment in QOL in most competitive swimmers. Such an increase is not related to seasonal allergen exposure in atopic athletes and probably results from chlorine derivative exposure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".