Diagnosis of Exercise-induced Bronchoconstriction in Swimmers: Context Matters
Bibliographic record
Abstract
Swimmers have a high prevalence of exercise-induced bronchoconstriction (EIB), which may be associated with repeated exposure to chlorinated pool water. The eucapnic voluntary hyperpnea (EVH) test is used to diagnose EIB; however, it fails to replicate the environmental conditions experienced by swimmers. The relationship between the composition of the EVH inspired gas and the development of EIB from swim exercise remains unclear. PURPOSE: This study aimed to compare the bronchoconstrictive effect of a chlorinated inspirate EVH test and swim test to a laboratory-based EVH test in swimmers. METHODS: Fifteen collegiate swimmers (n = 5 male, n = 10 female; 21 ± 2 yr) completed 3 d of testing in pseudorandom order; a standard EVH test (EVHL), a pool air EVH test (EVHCl), and a swimming test (Swim). Spirometry was measured at baseline, and 3, 5, 10, 15, and 20 min after each test. RESULTS: EVHL elicited a forced expired volume in 1 s (FEV1) fall index of -9.7% ± 6.4% compared with -6.6% ± 9.2% and -3.0% ± 7.5% after EVHCl and Swim, respectively (P < 0.05). Using Bland-Altman analysis, we found good agreement between EVHL and EVHCl (bias = -2.8, r = 0.79; P < 0.05) with poor agreement between EVHL and Swim (bias = -6.7, r = 0.20) and between EVHCl and Swim (bias = -3.9, r = 0.50; both P < 0.05). Forced expired flow between 25% and 75% lung volume and peak expired flow were significantly reduced by the EVHL compared with the EVHCl and Swim tests (P < 0.05). CONCLUSIONS: EVHL elicits a greater forced expired volume in 1-s fall index compared with EVHCl and Swim. The unique aquatic environment of swimmers potentially protects against bronchoconstriction and should be considered in the determination of EIB.
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 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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".