Validation of new potential targets for remission and low disease activity in psoriatic arthritis in patients treated with golimumab
Bibliographic record
Abstract
OBJECTIVES: Treat to target recommendations for PsA state that the target of treatment should be remission or, at the very least, low disease activity. Different clinical indexes have been proposed to define these disease states including the minimal disease activity criteria and the Disease Activity Index for PsA (DAPSA) scores, which have 7 and 4-5 domains, respectively. Using a Canadian cohort, the objectives were to calculate the proportion of patients achieving these criteria, their prognostic value and the overall patient impact of these disease states. METHODS: BioTRAC is an ongoing, prospective registry of inflammatory arthritis patients. 188 PsA patients treated with golimumab were included. Data collected at baseline, 6 and 12 months were used. RESULTS: Between 15.6% and 38.3% of patients achieved remission, and 37.4-77.7% achieved low disease activity at 6 and 12 months' follow-up. Patients achieving any minimal disease activity target and DAPSA low disease activity had significantly lower swollen joint count, tender joint count, psoriasis area and severity index, dactylitis and enthesitis scores compared with non-achievers (P < 0.05). Higher HAQ scores (P < 0.03) were observed in patients achieving remission with remaining dactylitis or active skin disease. CONCLUSION: Very low disease activity was the most stringent new potential target for remission in PsA. There was a high level of agreement between scores, although residual activity in dactylitis and skin despite DAPSA remission may affect patient function. Patients achieving either DAPSA endpoint, however, did not show a significant reduction in skin disease, indicating that those two criteria are more restricted to joint symptoms.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".