Application and Modifications of Minimal Disease Activity Measures for Patients with Psoriatic Arthritis Treated with Adalimumab: Subanalyses of ADEPT
Bibliographic record
Abstract
OBJECTIVE: This posthoc analysis evaluated the percentage of patients with psoriatic arthritis (PsA) who achieved minimal disease activity (MDA) and compared the results with a modified MDA substituting the physician global assessment (PGA) for the Psoriasis Activity and Severity Index (PASI) using data from the ADalimumab Effectiveness in Psoriatic Arthritis Trial (ADEPT; NCT00646386). METHODS: Patients with active PsA were randomized to receive adalimumab 40 mg or placebo every other week for 24 weeks. MDA was defined as achieving ≥ 5 of the following criteria: tender joint count ≤ 1; swollen joint count ≤ 1; PASI ≤ 1 or body surface area ≤ 3%; patient pain score ≤ 15 [1-100 mm visual analog scale (VAS)]; patient global assessment (PGA) of disease activity ≤ 20 (1-100 mm VAS); Health Assessment Questionnaire ≤ 0.5; and tender entheseal points ≤ 1 (only heels assessed). For modification of the MDA, PASI ≤ 1 was substituted with PGA "Clear" as MDAPGA1 and PGA "Clear" or "Almost clear" as MDAPGA2. RESULTS: Sixty-seven patients were treated with adalimumab and 69 with placebo. At Week 24, MDA, MDAPGA1, and MDAPGA2 were achieved by 39%, 37%, and 39%, respectively, of patients treated with adalimumab versus 7%, 5%, and 8% of patients on placebo (p < 0.001). Kappa coefficients indicated good agreement between PASI and PGA at Week 24. CONCLUSION: ADEPT results indicated that significantly more patients treated with adalimumab achieved MDA by Week 24 compared with placebo. Modification of the MDA by replacing PASI ≤ 1 with PGA assessments did not alter the results, which may improve feasibility of practical use of the index.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".