A network meta-analysis of the association between patient traits and response to regular dosing with ICS plus short-acting β2-agonist reliever or ICS/formoterol reliever only in mild asthma
Bibliographic record
Abstract
INTRODUCTION/BACKGROUND: -agonist reliever. Due to the heterogeneity of asthma, identification of traits associated with improved outcomes to specific treatments would be clinically beneficial. AIMS/OBJECTIVES: To assess the impact of patient traits on treatment outcomes of regular ICS dosing compared with intermittent ICS/formoterol dosing, a systematic literature review (SLR) and network meta-analysis (NMA) was conducted. Searches identified randomised controlled trials (RCTs) of patients with asthma aged ≥12 years, containing ≥1 regular ICS dosing or intermittent ICS/formoterol dosing treatment arm, reporting traits and outcomes of interest. RESULTS: The SLR identified 11 RCTs of mild asthma, of 14,516 patients. A total of 11 traits and 11 outcomes of interest were identified. Of these, a feasibility assessment indicated possible assessment of three traits (age, baseline lung function, smoking history) and two outcomes (exacerbation rate, change in lung function). The NMA found no significant association of any trait with any outcome with regular ICS dosing relative to intermittent ICS/formoterol dosing. Inconsistent reporting of traits and outcomes between RCTs limited analysis. CONCLUSIONS: This is the first systematic analysis of associations between patient traits and differential treatment outcomes in mild asthma. Although the traits analysed were not found to significantly interact with relative treatment response, inconsistent reporting from the RCTs prevented assessment of some of the most clinically relevant traits and outcomes, such as adherence. More consistent reporting of respiratory RCTs would provide more comparable data and aid 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 | 0.024 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.053 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".