A comparison of the effectiveness of biologic therapies for asthma: a systematic review and network meta-analysis
Bibliographic record
Abstract
Abstract Background Trials have not directly compared biologics for the treatment of asthma. Objective To comparative the relative efficacy of biologics in asthma. Methods We searched MEDLINE, EMBASE, CENTRAL, and clinicaltrials.gov from inception to May 31, 2022, for randomized trials addressing biologic therapies for asthma. Reviewers worked independently and in duplicate to screen references, extract data, and assess risk of bias. We performed a frequentist network meta-analysis and assessed the certainty of evidence using the GRADE approach. We present dichotomous outcomes as absolute risk differences per 1000 patients and relative risk (RR) with 95% confidence intervals (95% CI) and continuous outcomes as mean difference (MD) and 95% CI. Results We identified 64 trials, including 26,630 patients. For patients with eosinophilic asthma, tezepelumab (329 fewer exacerbations per 1000 [95% CI 272.6 to 366.6 fewer]) and dupilumab (319.6 fewer exacerbations per 1000 [95% CI 272.6 to 357.2 fewer]) reduce exacerbations compared to placebo (high certainty). Tezepelumab (MD 0.24 L [95% CI 0.16 to 0.32]) and dupilumab (0.25 L (95% CI 0.21 to 0.29) improve lung function (FEV1) compared to placebo (high certainty). Both tezepelumab (110.97 fewer hospital admissions per 1000 (95% CI 94.53 to 120.56 fewer) and dupilumab (97.27 fewer hospitalizations [4.11 to 124.67 fewer]) probably reduce hospital admissions compared to placebo (moderate certainty). For patients with low eosinophils, biologics probably do not improve asthma outcomes. For these patients, tezepelumab (MD 0.1 L [95% CI 0 to 0.19]) and dupilumab (MD 0.1 L [95% CI 0 to 0.20)] may improve lung function (low certainty). Conclusion Tezepelumab and dupilumab are effective at reducing exacerbations. For patients with low eosinophils, however, clinicians should probably be more judicious in use of biologics, including tezepelumab since they probably do not confer substantial benefit.
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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 | 0.042 | 0.092 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.052 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".