Predictors of placebo response in asthma attack rates: analysis from the ORACLE2 patient-level meta-analysis
Bibliographic record
Abstract
Rationale: In asthma randomised controlled trials (RCTs), a large placebo response hampers identification of treatment effects. Aim: To identify patient characteristics associated with placebo response in asthma attack rate in RCTs. Methods: We analyzed ORACLE2, a patient-level meta-analysis of RCT control arms, excluding open label RCTs, those without a placebo group, and those without prior attack data. Placebo response was defined as the difference in annualized asthma attack rate (pre-trial vs on-trial). To be appropriately modeled using Poisson regression, the placebo response was shifted to ensure all values were positive. Associations between baseline characteristics and placebo response were analyzed using a Poisson regression model, estimating adjusted rate ratios (aRR)[95% CI] while adjusting for pre-trial attacks. Results: Among 4381 patients from 14 RCTs, placebo response varied (0.6–2.2 attacks/year) and depended on prior attack criteria (Fig A). Lower placebo response was associated with higher blood eosinophils (per 10-fold increase: 0.97 [0.95–0.99]), exhaled nitric oxide (FeNO per 10-fold increase: 0.97 [0.95–1.00]) and low lung function (FEV₁ per 10% decrease: 0.99 [0.98–1.00]) (Fig B). Conclusions: Elevated type 2 biomarkers and impaired lung function are associated with less placebo response on attack in asthma RCTs. Prospero: CRD42021245337 FUNDING: NIHR, FRQS, APQ, AcMedSci erj;66/suppl_69/PA4691/F1 F1 F1
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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 | 0.040 | 0.067 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.060 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".