A network meta-analysis study of monotherapies for hidradenitis suppurativa: analyses of the current evidence base
Bibliographic record
Abstract
Background The number of monotherapies for hidradenitis suppurativa (HS) has expanded. However, the efficacy of active comparators has not been determined in head-to-head trials.Aims We conducted an NMA to determine the relative efficacy and safety of monotherapies for HS.Methods The literature was systematically reviewed to obtain data from trials that (1) were published in English, (2) investigated a systemically administered monotherapy with an immunomodulatory agent (3) randomized, and (4) quantified efficacy, at 16 weeks, insofar as the Hidradenitis Suppurativa Clinical Response 50 (HiSCR-50), Dermatology Life Quality Index (DLQI) and Numeric Rating Scale 30 (NRS30). For safety, we analyzed the occurrence of treatment-emergent adverse events (TEAEs). For sensitivity analyses, we conducted network meta-regressions adjusted for age and sex.Results We determined the efficacy of numerous regimens including those approved by the United States FDA; for instance, the FDA-approved ‘bimekizumab 320 mg every 2 weeks’ was more efficacious than ‘IFX-1 800 mg every 2 weeks’ (odd ratio = 1.99, 95% credible interval: 1.09,3.87, p < 0.05) in terms of HiSCR-50. Sensitivity analyses showed that the main analyses were robust. Overall, risk of bias across studies was low.Conclusions The current NMA provides comparative evidence on systematic immunomodulatory HS monotherapies from the most up-to-date trial evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.076 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.055 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".