Minimal Disease Activity and Anti–Tumor Necrosis Factor Therapy in Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: A state of minimal disease activity (MDA) was defined and validated as target for treatment in psoriatic arthritis (PsA). We aimed to identify disease characteristics, outcome, and predictors of MDA in patients treated with tumor necrosis factor α (TNFα) blockers. METHODS: Patients fulfilling the Classification of Psoriatic Arthritis criteria treated with TNFα blockers were followed every 3-6 months. Patients were considered in MDA when they meet at least 5 of the 7 criteria. Sustained MDA was defined as an MDA state lasting ≥12 months. Patients achieving MDA were compared to non-MDA patients. A proportional odds discrete time survival analysis model was applied, adjusting for sex, age, PsA duration, abnormal erythrocyte sedimentation rate (ESR) and clinically damaged joint count at each visit to identify predictors for MDA. RESULTS: Of the 306 patients treated with TNFα blockers identified from our database, 23 patients were in an MDA state when treatment was commenced; 57 were taking TNFα blockers prior to enrollment. Therefore, 226 subjects were in a non-MDA state and constituted the study population. One hundred forty-five patients of 226 patients (64%) achieved MDA within a mean ± SD duration of 1.30 ± 1.68 years. The mean ± SD duration of MDA was 3.46 ± 2.25 years. At total of 17 patients withdrew from therapy and remained in an MDA state. Male sex (odds ratio [OR] 1.65, 95% confidence interval [95% CI] 1.08-2.53; P = 0.02) and normal ESR (OR 2.27, 95% CI 1.22-4.17; P = 0.009) increased the odds for achieving MDA. CONCLUSION: MDA is achieved in 64% of patients treated with TNFα blockers in a clinical setting. Male sex and normal ESR are predictors for MDA. On withdrawal or reduction in treatment, 11.6% of patients maintained MDA state.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".