Field versus race pace conditions to provoke exercise-induced bronchoconstriction in elite swimmers: Influence of training background
Bibliographic record
Abstract
Diagnosing Airway hyper-responsiveness (AHR) requires bronchial provocation tests that are performed at rest and after exercise or hyperventilation in either a lab or field setting. Presently, it is unclear whether the proposed AHR field test for swimming induces sufficient provocation due to lack of intensity. Thus we aimed to examine how the 8 minute field swim test compared to all out racing and a lower intensity practice exposure affected AHR. We hypothesized that the race would affect AHR the most thereby highlighting the importance of maximal effort in swim AHR. 10 female and 15 male swimmers completed three conditions (sanctioned race of different distances, 8 min field swim challenge and swim practice). Forced vital capacity (FVC), forced expired volume in 1 second (FEV1) and forced expiratory flow (FEF25-75) were measured at rest and after each exercise condition (at 6 and 10 min) in accordance with standard protocols. AHR was defined as a decrease in FEV1 of ≥10% post exercise. A significant increase in FEV1 and FEF25-75 was observed for both post swim field test and post-race. The practice condition reduced FEV1 in 44% of swimmers although the magnitude of change was small. There was a wide variability in the individual responses to the 3 conditions and AHR was diagnosed in one swimmer (race condition). All conditions have poor sensitivity to diagnose EIB and total accumulated ventilation (distance swum) did not influence AHR. These results also indicate that elite swimmers, despite many risk factors, are not limited by respiratory function in race conditions. It is proposed that the swim field test not be used for AHR assessment in swimmers due to too high relative humidity.
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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) | 0.001 | 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".