Mannitol-evoked cough response is reduced by inhaled salbutamol
Bibliographic record
Abstract
Background Mannitol inhalation induces bronchoconstriction and coughing in asthmatic patients. Salbutamol causes smooth muscle relaxation in acute asthma, but its effects on airway nerves are unclear. We conducted mannitol cough challenges in allergic asthmatic patients (AAs) and healthy controls (HCs) to determine whether salbutamol would reduce mannitol-evoked coughs independent of changes in airway calibre. Methods A double-blind, randomised, placebo-controlled, two-way crossover study included 20 mild steroid-naïve AAs and 20 HCs. The maximum number of coughs (Emax) evoked by mannitol was compared between salbutamol and placebo. Results HCs and AAs were matched for age, lung function and sex. Mannitol-evoked cough dose ratio (CDR) in HCs was significantly lower than in AAs (5.63±1.84versus7.32±3.67 coughs, p=0.023). Both groups had similar Emax, C2 and C5 (dose causing two and five coughs, respectively). In AAs mannitol decreased forced expiratory volume in 1 s (FEV1) by 8.11±2.32% with placebo, which was attenuated to 3.19±1.16% with salbutamol (p=0.03). In HCs, FEV1reduction was not significant. Emax in AAs for placebo and salbutamol was 8.90 (8.09–9.80) coughs and 7.07 (6.28–8.00) coughs, respectively (p<0.001) and in HCs 8.19 (7.08–9.61) coughs and 4.95 (4.38–5.66) coughs, respectively (p<0.001). The effect of salbutamol on Emax between groups was significantly different (p<0.001). Salbutamol improved CDR, C2 and C5 in AAs (p<0.05) but without effect between groups. Conclusion Salbutamol reduced mannitol Emax in AAs and HCs. The reduced mannitol-evoked cough response by salbutamol in HCs was independent of changes in airway calibre measured by FEV1, suggesting mechanisms of salbutamol extend beyond smooth muscle relaxation.
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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) | 0.001 | 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".