A network meta-analysis of the association between patient traits and response to regular dosing with ICS/long-acting β2-agonist plus short-acting β2 agonist reliever or maintenance and reliever therapy for asthma
Bibliographic record
Abstract
Introduction Current treatment for moderate–severe asthma with inhaled corticosteroid (ICS)-based therapy can follow two strategies: a single inhaler maintenance and reliever therapy (MART) regimen, or regular dosing with ICS/long-acting β 2 -agonist used as maintenance therapy plus a separate short acting β 2 -agonist reliever inhaler. It would be clinically useful to understand the potential of patient traits to influence regular dosing or MART treatment outcomes. Objectives A systematic literature review (SLR) and meta-analysis was conducted to identify specific patient traits that may predict improved clinical outcomes with regular dosing or MART. Results The SLR identified 28 studies in patients with moderate–severe asthma assessing regular dosing or MART treatments and reporting the traits and outcomes of interest. Network meta-regressions found no significant difference in the relative efficacy of regular dosing as compared with MART on any of the clinical outcomes (exacerbation rate, time to first exacerbation, FEV 1 , reliever use and adherence) for any of the patient traits (baseline lung function, baseline ACQ, age, BMI, and smoking history) evaluated. However, some trends towards traits influencing treatment efficacy were identified. Inconsistent reporting of traits and outcomes was observed between trials. Conclusions The analysed patient traits evaluated in this study were associated with similar efficacy for the analysed outcomes to either regular dosing or MART; however, trends from the data observed encourage future analyses for possible identification of additional traits, or a combination of traits, that may be of interest. More comparable reporting of clinically important traits and outcomes would improve future analyses.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.054 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".