Time and predictors of response to tumour necrosis factor- blockers in psoriatic arthritis: an analysis of a longitudinal observational cohort
Bibliographic record
Abstract
OBJECTIVE: To determine predictors and time to response to treatment with TNF-alpha blockers in patients with PsA in a longitudinal observational cohort. METHODS: We performed a cohort analysis of patients who were followed prospectively in a large PsA clinic. Response to treatment was defined as an improvement of at least 40% in active (tender and/or swollen) and swollen joint count (SJC) and 50% improvement in the Psoriasis Area and Severity Index (PASI) score. RESULTS: Ninety-five patients were included in the analysis. Of the total patients, 72.6 and 77.9% demonstrated 40% improvement in active joint counts at 3 and 12 months, respectively. Also, 80.5 and 87.4% of the patients showed 40% improvement in SJC at 3 and 12 months, respectively. A PASI50 was achieved by 54 and 60.4% after 3 and 12 months of treatment, respectively. Of 17 patients who did not achieve 40% improvement in total SJC at 3 months, 11 (64.7%) responded at 12 months. In multivariate analysis, the number of swollen joints at baseline predicted response of total active joints at 12 months [odds ratio (OR) 1.34; P = 0.02], whereas past use of TNF-alpha blocker decreased odds of response (OR 0.05; P = 0.01). CONCLUSION: TNF-alpha blockers are effective in most PsA patients with the majority responding within 3 months of treatment. A significant proportion of the early non-responders will have a delayed response to treatment. Higher SJC at baseline and no prior use of TNF-alpha blockers predict response.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".