Time to Disease Control with Dupilumab for Bullous Pemphigoid: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Bullous pemphigoid (BP) is a chronic autoimmune blistering disease primarily affecting the elderly population. While awaiting the results from randomized clinical trials to assess the effect of dupilumab in patients with BP, clinicians have begun to introduce dupilumab into their therapeutic arsenal, with few data supporting their clinical decisions. The objectives were to assess time to disease control, predictors of response, achievement of disease control, disease recurrence, and occurrence of adverse events. Randomized and non-randomized studies of interventions (NRSIs) from Medline and Embase were reviewed. A total of 315 studies were identified and 5 NRSIs (167 participants with moderate-to-severe BP) meeting our inclusion criteria were found. Dupilumab was significantly associated with shorter time to disease control compared with the control group (HR 2.71 [95% CI, 1.85-3.96; I2 = 35%; 127 participants; 4 studies]). The overall strength of the evidence was graded as very low due to serious risk of bias and imprecision of effect measures. There were insufficient data to inform conclusions regarding BP recurrence and adverse events. Evidence was found that dupilumab reduces time to disease control in BP. It was not possible to assess predictors of response using pre-planned meta-regression. Randomized controlled trials are needed to determine dupilumab's place in therapeutic algorithms for BP. PROSPERO number: CRD42024599235.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".