Facial Cooling Enhances Exercise-Induced Bronchoconstriction in Asthmatic Children
Bibliographic record
Abstract
PURPOSE: Exercising in cold air enhances bronchial responsiveness (BR) as compared with exercising in warm air. This may be due to intrathoracic cooling or to increased vagal activity caused by facial cooling. The purpose of this study was to compare the effects on BR of cold air inhalation and of facial exposure to cold air, as well as the combined effect of both. METHODS: Fourteen children with asthma (eight girls) performed four exercise challenge tests in a climatic chamber, under one of the following conditions: 1) inhaling warm air while the face was exposed to warm air (WW, 21 degrees C, 25% relative humidity (RH)); 2) inhaling warm air while the face was exposed to cold air (WC, 0 degrees C, 80% RH); 3) inhaling cold air while the face was exposed to cold air (CC); and 4) inhaling cold air while the face was exposed to warm air (CW). The study was analyzed, using a one- and two-way ANOVA. RESULTS: Postexercise forced expiratory volume in the first second (FEV1) and maximal mid-expiratory flow (MMEF) values as percent predicted (% pred) showed significant reductions over time (P < 0.001), significant differences among the four experimental conditions (P < 0.001) and a significant condition x time interaction (FEV1:P < 0.001, MMEF:P < 0.01). FEV1 was significantly lower for CC and WC, as compared with WW and CW at 5 and 10 min postexercise. The lowest postexercise values for FEV1 occurred in the CC and WC sessions (76% predicted in both). A similar pattern was obtained for MMEF. CONCLUSION: Facial cooling combined with either cold or warm air inhalation causes the greatest EIB, as compared with the isolated challenge with cold air inhalation. We suggest that vagal mechanisms play a major role in exercise and cold-induced bronchoconstriction.
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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".