Performance of composite measures used in a trial of etanercept and methotrexate as monotherapy or in combination in psoriatic arthritis
Bibliographic record
Abstract
OBJECTIVES: To examine which composite measures are most sensitive to change when measuring psoriatic arthritis (PsA) disease activity, analyses compared the responsiveness of composite measures used in a 48-week randomized, controlled trial of MTX and etanercept in patients with PsA. METHODS: The trial randomised 851 patients to receive weekly: MTX (20 mg/week), etanercept (50 mg/week) or MTX plus etanercept. Dichotomous composite measures examined included ACR 20/50/70 responses, minimal disease activity (MDA) and very low disease activity (VLDA). Continuous composite measures examined included Disease Activity Score (28 joints) using CRP (DAS28-CRP), Clinical Disease Activity Index (CDAI), Simplified Disease Activity Index (SDAI), Disease Activity for Psoriatic Arthritis (DAPSA) and Psoriatic Arthritis Disease Activity Score (PASDAS). RESULTS: At week 24, etanercept-treated groups were significantly more effective than MTX monotherapy to achieve ACR 20 (primary end point) and MDA (key secondary end point). When examining score changes from baseline at week 24 across the five continuous composite measures, PASDAS demonstrated relatively greater changes in the etanercept-treated groups compared with MTX monotherapy and had the largest effect size and standardized response. Joint count changes drove overall score changes at week 24 from baseline in all the continuous composite measures except for PASDAS, which was driven by the Physician and Patient Global Assessments. CONCLUSION: PASDAS was the most sensitive continuous composite measure examined with results that mirrored the protocol-defined primary and key secondary outcomes. Composite measures with multiple domains, such as PASDAS, may better quantify change in PsA disease burden. TRAIL REGISTRATION: https://ClinicalTrials.gov, number NCT02376790.
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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".