Minimal Disease Activity and Remission in Psoriatic Arthritis Patients Treated with Anti-TNF-α Drugs
Bibliographic record
Abstract
OBJECTIVE: A state of remission is the target of therapy in chronic arthritis. The aim of the present study was to assess the rate of minimal disease activity (MDA) and remission in patients with psoriatic arthritis (PsA) treated with tumor necrosis factor (TNF-α) blockers. Disease characteristics and predictors of MDA were also evaluated. METHODS: Patients fulfilling the ClASsification for Psoriatic ARthritis (CASPAR) criteria and treated with TNF-α blockers adalimumab, etanercept, or golimumab were enrolled and prospectively followed every 4 months for 1 year in a clinical practice setting. Patients were considered in MDA when they met at least 5/7 of the criteria previously defined. Other remission criteria evaluated were 28-joint Disease Activity Score-C-reactive protein (DAS28-CRP) < 2.6 and Disease Activity in Psoriatic Arthritis (DAPSA) score ≤ 3.3. Patients achieving MDA were compared to non-MDA to identify outcome predictor factors. RESULTS: Of the 75 patients treated with TNF-α blockers, at baseline no patients were in MDA or had a DAPSA score ≤ 3.3, while 25 (21.3%) had a DAS28-CRP score < 2.6. Five patients (6%) discontinued treatment because of side effects or inefficacy during followup. After 12 months, MDA was achieved in 46 patients (61.3%). No difference was found among the 3 anti-TNF-α drugs. Predictors for MDA were found to be male sex, high CRP, high erythrocyte sedimentation rate, and low Health Assessment Questionnaire. CONCLUSION: In our prospective observational study, based on a clinical practice setting, MDA was achieved in 61.3% of patients treated with TNF-α blockers, identifying this as an achievable target for patients with PsA. Predictors of remission were also identified.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".