Factors predicting persistence of biologic drugs in psoriasis: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Long-term therapy for psoriasis is impaired by gradual loss of effectiveness and treatment discontinuation. Identifying factors that affect biologic drug survival may help in treatment optimization. OBJECTIVES: To identify factors that predicted biologic drug persistence or discontinuation in a real-life setting. METHODS: We identified studies of biologic persistence in psoriasis through a comprehensive, systematic literature search using predefined search criteria. Studies were screened by title and abstract then further by full-text review. Hazard ratio (HR) data were extracted for all available predictive factors (HRs > 1 denoted biologic discontinuation, and HRs < 1 denoted biologic persistence). A meta-analysis of HRs (random-effects model) was used to assess any predictive factor included in at least two studies. RESULTS: = 67%). Other reported predictive factors (smoking, metabolic syndrome, biologic naivety, age, Dermatology Life Quality Index, dyslipidaemia, high socioeconomic status and concomitant methotrexate) were insufficiently reported for meta-analysis. CONCLUSIONS: Our meta-analysis demonstrates that female sex and obesity predict biologic discontinuation, and concomitant psoriatic arthritis predicts biologic survival. What's already known about this topic? Ineffectiveness is the main factor that causes drug discontinuation during long-term treatment of psoriasis. It is unclear which factors and comorbidities impact drug persistence. What does this study add? Female sex and obesity predict biologic discontinuation due to ineffectiveness and adverse events. Concomitant psoriatic arthritis is associated with improved drug persistence.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".