Prediction and benefits of minimal disease activity in patients with psoriatic arthritis and active skin disease in the ADEPT trial
Bibliographic record
Abstract
OBJECTIVES: To determine the proportion of patients with psoriatic arthritis in the Adalimumab Effectiveness in Psoriatic Arthritis trial achieving minimal disease activity (MDA) and its individual components at 1 or more visits over 144 weeks, identify baseline predictors of MDA achievement, and evaluate the association of MDA status with independent quality of life (QoL)-related patient-reported outcomes (PROs). METHODS: Univariate and multivariate analyses were used to identify the baseline characteristics that predicted achievement of MDA at individual time points (weeks 12 through 144) or sustained MDA (achievement of MDA at 2 consecutive time points 12 weeks apart). The association of independent QoL-related PROs with MDA achievement was evaluated at weeks 24 and 144. RESULTS: In univariate analyses, higher baseline patient assessment of pain, tender joint count (TJC), enthesitis and Health Assessment Questionnaire-Disability Index (HAQ-DI) score were significantly associated with lower likelihood of achieving MDA at later time points. Multivariate analyses confirmed higher baseline HAQ-DI as a significant predictor for failure to achieve MDA at later time points. Achievement of sustained MDA was associated with lower baseline TJC and HAQ-DI score. Achievement of different MDA components appeared to be treatment dependent. MDA achievers had significantly better QoL-related PROs and greater improvements in PROs from baseline to week 24 compared with non-achievers. CONCLUSIONS: Higher HAQ-DI score was the most consistent baseline factor that decreased the likelihood of achieving MDA and sustained MDA at later time points. Achieving MDA was associated with better independent QoL-related PROs.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".