Effect of different asthma treatments on risk of cold-related exacerbations
Bibliographic record
Abstract
Common colds often trigger asthma exacerbations. The present study compared cold-related severe exacerbations during budesonide/formoterol maintenance and reliever therapy, and different regimens of maintenance inhaled corticosteroids (ICS), with or without long-acting β(2)-agonists (LABA), and with as-needed short-acting β(2)-agonists (SABA) or LABA. Reported colds and severe exacerbations (defined by oral corticosteroid use and/or hospitalisation/emergency room visit) were assessed for 12,507 patients during 6-12 months of double-blind treatment. Exacerbations occurring ≤14 days after onset of reported colds were analysed by a Poisson model. The incidence of colds was similar across treatments. Asthma symptoms and reliever use increased during colds. Budesonide/formoterol maintenance and reliever therapy reduced severe cold-related exacerbations by 36% versus pooled comparators plus SABA (rate ratio (RR) 0.64; p=0.002), and for individual treatment comparisons, by 52% versus the same maintenance dose of ICS/LABA (RR 0.48; p<0.001); there were nonsignificant reductions versus higher maintenance doses of ICS or ICS/LABA (RR 0.83 and 0.72, respectively). As-needed LABA did not reduce cold-related exacerbations versus as-needed SABA (RR 0.96). Severe cold-related exacerbations were reduced by budesonide/formoterol maintenance and reliever therapy compared with ICS with or without LABA and with as-needed SABA. Subanalyses suggested the importance of the ICS component in reducing cold-related exacerbations. Future studies should document the cause of exacerbations, in order to allow identification of different treatment effects.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".