The Impact of Tumor Necrosis Factor α Inhibitors on Radiographic Progression in Ankylosing Spondylitis
Bibliographic record
Abstract
OBJECTIVE: To study the effect of tumor necrosis factor α (TNFα) inhibitors on progressive spinal damage in patients with ankylosing spondylitis (AS). METHODS: All AS patients meeting the modified New York criteria who had been monitored prospectively and had at least 2 sets of spinal radiographs a minimum of 1.5 years apart were included in the study (n=334). The patients received standard therapy, which included nonsteroidal antiinflammatory drugs and TNFα inhibitors. Radiographic severity was assessed by the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS). Patients with a rate of AS progression that was ≥1 mSASSS unit/year were considered progressors. Univariable and multivariable regression analyses were done. Propensity score matching and sensitivity analysis were performed. A zero-inflated negative binomial (ZINB) model was used to analyze the effect of TNFα inhibitors on the change in the mSASSS with varying followup periods. Potential confounders, such as disease activity (as assessed by the Bath Ankylosing Spondylitis Disease Activity Index), the erythrocyte sedimentation rate, C-reactive protein level, HLA-B27 positivity, sex, age at onset, smoking burden (number of pack-years), and baseline damage, were included in the model. RESULTS: TNFα inhibitor treatment was associated with a 50% reduction in the odds of progression, with an odds ratio (OR) of 0.52 (95% confidence interval [95% CI] 0.30-0.88, P=0.02). Patients with a delay of >10 years in starting therapy were more likely to experience progression as compared to those who started earlier (OR 2.4 [95% CI 1.09-5.3], P=0.03). In the ZINB model, the use of TNFα inhibitors significantly reduced disease progression when the gap between radiographs was >3.9 years. The protective effect of TNFα inhibitors was stronger after propensity score matching. CONCLUSION: Treatment with TNFα inhibitors appears to reduce radiographic progression in AS patients, especially with early initiation and with longer duration of followup.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".