Network meta‐analyses of systemic treatments for psoriasis: a critical appraisal
Bibliographic record
Abstract
AIM: There are numerous systemic medications in use for psoriasis, with additional investigational agents being studied. However, head-to-head, randomized clinical trials are rare and cannot feasibly compare all treatments. A network meta-analysis (NMA) synthesizes the available evidence to provide estimates for all pairwise comparisons. Here, we summarize and appraise two recent NMAs that assessed systemic therapies for moderate-to-severe psoriasis. SETTING AND DESIGN: Two systematic reviews searched databases and the grey literature to identify relevant randomized clinical trials. STUDY PARTICIPANTS: The reviews mostly included trials that involved adults with moderate-to-severe psoriasis. One of the reviews also included two trials involving children. STUDY EXPOSURE: Interventions common to both reviews include adalimumab, etanercept, infliximab, ustekinumab, ixekizumab, secukinumab and methotrexate. One of the reviews included additional interventions, primarily other biological agents along with new small-molecule treatments and systemic conventional treatments. PRIMARY OUTCOMES: One review focused on 'clear/nearly clear' and withdrawals from adverse events as study outcomes, while the second review focused on improvement of ≥ 90% measured on the Psoriasis Area and Severity Index (PASI 90) and serious adverse events. OUTCOMES: Additional outcomes included quality of life, PASI 75, Physician's Global Assessment of 0/1 and any adverse event. RESULTS: Overall, both NMAs are of high quality and provide a comprehensive summary of the evidence base and treatment effects. Results, in terms of both estimates and rankings, suggest that newer biologics targeting the interleukin (IL)-12/23 and IL-17 axes appear to be more effective than older biologics and oral agents. CONCLUSIONS: Patients, clinicians and policy makers can use the relative efficacy assessments of NMAs to inform decision making regarding the clearance of psoriasis skin lesions at relevant time points and improvement in quality of life.
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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 | 0.079 | 0.175 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 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".