Persistence of low disease activity after tumour necrosis factor inhibitor (TNFi) discontinuation in patients with psoriatic arthritis
Bibliographic record
Abstract
OBJECTIVE: To determine the duration of clinical benefit among patients with psoriatic arthritis (PsA) discontinuing tumour necrosis factor inhibitor (TNFi) therapy while in low disease activity (LDA), and to identify patient characteristics associated with prolonged clinical benefit. METHODS: We performed an observational cohort study assessing patients with PsA from the Consortium of Rheumatology Researchers of North America (CORRONA) registry who had discontinued TNFi after achieving LDA, defined as clinical disease activity index (CDAI) score ≤10 and physician's global assessment (PGA) of skin psoriasis ≤20/100. Kaplan-Meier method was used to estimate the duration of clinical benefit. RESULTS: Of the 5945 patients with PsA in CORRONA, 302 patients had discontinued TNFi (n=325) while in LDA and had follow-up data available. At time of discontinuation, mean PsA duration was 9.8 years, mean CDAI was 3.9, and mean duration of TNFi use was 1.5 years; 52.6% of patients had discontinued their first TNFi. Median time to loss of benefit was 29.2 months. 179 (55.1%) patients had persistent benefit at their previous clinic visit. An increased risk of losing clinical benefit was seen among patients with higher disease activity at discontinuation (CDAI≥3.2 vs <3.2; HR 1.43 (p=0.32)) and among smokers (HR 1.78 (p=0.027)). CONCLUSIONS: Patients with PsA who achieve LDA may maintain clinical benefit after discontinuation of TNFi therapy.
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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) | 0.001 | 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 | 0.000 |
| 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".