Effectiveness and Drug Survival of TNF Inhibitors in the Treatment of Ankylosing Spondylitis: A Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: The aim of this research was to describe the effectiveness and drug survival of tumor necrosis factor (TNF) inhibitors in the treatment of ankylosing spondylitis (AS) and to analyze the effect of concomitant treatment with conventional disease-modifying antirheumatic drugs. METHODS: Patients with AS identified from the National Register for Biologic Treatment in Finland starting their first TNF inhibitor treatment between July 2004 and December 2011 were included. Treatment response was measured as an improvement of 50% (or 20 mm) after 6 months of treatment onset compared to the baseline Bath AS Disease Activity Index (BASDAI) score. Treatment response and 2-year drug survival were modeled with logistic regression and time-dependent Cox proportional hazard models, respectively. RESULTS: The study comprised 543 patients, of whom 123 also commenced a second TNF inhibitor during the followup. Treatment was discontinued within 24 months by 25% and 28% of the users of the first and the second TNF inhibitors, respectively. BASDAI response at 6 months was achieved by 52% and 25% of the users of the first and the second TNF inhibitors, respectively. Etanercept (ETN; HR 0.42, 95% CI 0.29-0.62) and adalimumab (ADA; HR 0.48, 95% CI 0.30-0.77) were associated with better drug survival in comparison to infliximab (IFX). Also, concurrent use of sulfasalazine (SSZ; HR 0.70, 95% CI 0.49-0.99) decreased the hazard for treatment discontinuation. CONCLUSION: TNF inhibitors are equipotent in the treatment of AS; however, ETN and ADA were found superior to IFX in drug survival. The use of SSZ improves treatment continuation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".