Prevalence of Psoriatic Arthritis Patients Achieving Minimal Disease Activity in Real-world Studies and Randomized Clinical Trials: Systematic Review with Metaanalysis
Bibliographic record
Abstract
Objective. To estimate the frequency of patients with psoriatic arthritis (PsA) achieving minimal disease activity (MDA) status in real-world studies and randomized controlled trials (RCT). Methods. A systematic literature search for 2009–2017 was performed in PubMed, Embase, Cochrane Library, and LILACS. Study selection and data extraction were performed by 2 independent researchers. Random-effects single-arm metaanalyses were performed and heterogeneity was assessed using I2. Results. A total of 405 records were identified and 45 studies were analyzed: 39 (86.7%) observational studies and 6 (13.3%) RCT; they included 12,469 patients. The overall prevalence of MDA in cross-sectional studies was 35% (95% CI 30%–41%, I2 = 94%), varying from 17% (95% CI 7%–34%) in patients taking synthetic disease-modifying antirheumatic drugs (DMARD) to 57% (95% CI 41%–71%) in those taking biological DMARD. Prevalence of MDA in cohort studies increased with longer followup time, ranging from 25% (95% CI 15%–40%) with 3- to 4-month followup to 42% (95% CI 38%–45%) with > 24-month followup. Patients with PsA receiving biological DMARD in a real-world context and RCT had similar prevalence of MDA at 6-month followup: 30% (95% CI 21%–41%, I2 = 85%) versus 32% (95% CI 26%–39%, I2 = 79%), respectively. Conclusion. Patients with PsA included in real-world studies had similar prevalence of MDA compared to those in controlled clinical trials. This finding suggests that MDA is a useful treatment target for PsA in the real-world setting.
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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 | 0.060 | 0.153 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.032 | 0.042 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".