Patient Profiles in Randomized Controlled Trials Versus a Real-World Study in Psoriatic Arthritis: Scoping Review and Metaanalysis
Bibliographic record
Abstract
OBJECTIVE: Patients with psoriatic arthritis (PsA) in randomized controlled trials (RCTs) may not reflect patients with PsA in clinical practice. Our objective was to perform a metaanalysis comparing the characteristics of patients with PsA in RCTs of biologic disease-modifying antirheumatic drugs (bDMARDs) to patient profiles in a real-world study. METHODS: Data sources included (1) a scoping literature review of phase III RCTs of bDMARDs in PsA published between 2015 and 2020, and (2) an international observational study of patients with PsA starting a bDMARD enrolled between 2015 and 2018 (PsABio; ClinicalTrials.gov: NCT02627768). Data collected at baseline included swollen and tender joint counts (SJC/TJC), presence of enthesitis, skin involvement (body surface area [BSA]), C-reactive protein (CRP), physician global assessment (PGA), and patient-reported outcomes (PROs; Health Assessment Questionnaire [HAQ], pain). Univariate random effects metaanalysis was conducted to calculate pooled means and proportions. RESULTS: Overall, 5654 patients from 10 RCTs were compared to 930 PsABio patients. Demographic data were similar. SJC/TJC were higher in RCTs than in PsABio (pooled means: 11.8/21.5 vs 5.7/11.9), and enthesitis was more frequent in RCTs (64.7% vs 48.2%), as were patients with a BSA ≥ 3% (62.2% vs 54%). PGA was higher in RCTs (59.7 vs 54.1). In contrast, PROs were similar, whereas CRP was significantly higher in PsABio (1.4 vs 1.1 mg/dL). CONCLUSION: Patients with PsA starting a bDMARD in RCTs had highly active disease and a high patient-reported disease burden. In contrast, PsABio real-world patients starting a bDMARD had lower SJC/TJC, skin involvement, and PGA, but presented with similar patient-reported disease burden. The extrapolation of RCT data in clinical practice should take these elements into account.
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 | 0.144 | 0.245 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.028 | 0.050 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".