Low Dose Inhaled Budesonide and Formoterol in Mild Persistent Asthma
Bibliographic record
Abstract
The optimal treatment for mild asthma is uncertain. We assessed the effects of adding a long-acting inhaled beta-agonist, formoterol, to low doses of an inhaled corticosteroid, budesonide, for 1 yr in subjects with mild asthma, receiving no or only a small dose of inhaled corticosteroid. The 698 corticosteroid free patients (Group A) were assigned to twice daily treatment with 100 microg budesonide, 100 microg budesonide plus 4.5 microg formoterol, or placebo. The 1,272 corticosteroid-treated patients (Group B) were assigned to twice daily treatment with 100 microg budesonide, 100 microg budesonide plus 4.5 microg formoterol, 200 microg budesonide, or 200 microg budesonide plus 4.5 microg formoterol. The main outcome variables were time to the first severe asthma exacerbation and poorly controlled asthma days. In Group A, budesonide alone reduced the risk for severe exacerbations by 60% and poorly controlled days by 48%; adding formoterol increased lung function with no change in other end points. By contrast, in Group B, adding formoterol reduced the risk for the first severe exacerbation and for poorly controlled days by 43 and 30%, respectively. Thus, in corticosteroid-free patients, low dose inhaled budesonide alone reduced severe exacerbations and improved asthma control, and in patients already receiving inhaled corticosteroid, adding formoterol was more effective than doubling the corticosteroid dose.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".