Effectiveness and Feasibility Associated with Switching to a Second or Third TNF Inhibitor in Patients with Psoriatic Arthritis: A Cohort Study from Southern Sweden
Bibliographic record
Abstract
OBJECTIVE: Because new modes of action for the treatment of psoriatic arthritis (PsA) are emerging, it is important to understand the use of switching to a second or third antitumor necrosis factor (anti-TNF) agent. This study investigated drug survival and treatment response rates of patients with PsA undergoing second- and third-line anti-TNF therapy. METHODS: Patients with PsA were monitored in a prospective, observational study. Patients who switched anti-TNF therapy once (first-time switchers, n = 217) or twice (second-time switchers, n = 57) between January 2003 and March 2012 were studied. American College of Rheumatology (ACR) and European League Against Rheumatism (EULAR) good response at 3 and 6 months, as well as drug survival, were reported and further analyzed using the Cox and logistic regression models. RESULTS: Median age for first-time switchers was 47 years and 42% were men. The corresponding values for second-time switchers were 48 years and 40% men. Three-month ACR20 Lund Efficacy Index (LUNDEX) response was achieved by 47% of first-time and 22% of second-time switchers; ACR50 LUNDEX rates were 21% and 14%, ACR70 LUNDEX rates were 12% and 2%, and EULAR good LUNDEX rates were 26% and 10%, respectively. Median drug survival time for patients switching anti-TNF for the first time was 64 months (95% CI 31-97) compared with 14 months (95% CI 5-23) for second-time switchers. Identified baseline predictor of ACR20 response to second-line treatment was the 28-joint Disease Activity Score values at baseline (OR 1.45, 95% CI 1.01-2.10), while higher Health Assessment Questionnaire scores predicted premature drug withdrawal (HR 1.60, 95% CI 1.03-2.48). CONCLUSION: Response rates of first-time anti-TNF switchers are moderate, while the inferior response rates of second-time switchers suggest other therapeutic options should be considered in this situation.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".