Biologic and Non-Biologic Therapies for Scalp Psoriasis: A Network Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
INTRODUCTION: Scalp psoriasis affects up to 80% of patients with plaque-type psoriasis and is often resistant to topical and conventional systemic agents. There is a lack of consensus on a "gold standard" treatment. OBJECTIVE: This comprehensive review and network meta-analysis aimed to compare the efficacy and safety of studied interventions. METHODS: The Ovid MEDLINE(R), Embase, and Cochrane databases were searched from 01 January 2000 to 05 October 2022. All English-language randomized controlled trials evaluating an intervention for scalp psoriasis were included if they reported one of the following clinical outcomes: Psoriasis Scalp Severity Index (PSSI), scalp Physician Global Assessment (ScPGA), scalp-specific Investigator or Physician Global Assessment (IGA/PGA), and Total Sign Score (TSS), and adverse events. A random effects network meta-analysis was performed where possible, and network plots were generated. RESULTS: Of 1,046 studies identified, 35 met the inclusion criteria, with seven in the PSSI analysis and 16 in the IGA analysis. All interventions led to an improvement in all outcomes when compared to placebo in the PSSI and PGA/IGA. For the PSSI response, secukinumab 300 mg every four weeks (Q4W) was the most effective (SUCRA 0.991). For the PGA/IGA response, bimekizumab 320 mg Q4W was the most effective (SUCRA 0.975). CONCLUSIONS: Several systemic therapies are superior to placebo in improving clinical outcomes, with secukinumab 300 mg Q4W and bimekizumab 320 mg Q4W deemed the most effective among biologic agents analyzed. Efforts to enhance research standardization, including head-to-head trials with standardized outcome measures, diverse patient recruitment, and long-term follow-up, are crucial next steps in assessing treatment efficacy and adverse events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.061 | 0.020 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".