Dosage Adjustment of Anti-Tumor Necrosis Factor-α Inhibitor in Ankylosing Spondylitis Is Effective in Maintaining Remission in Clinical Practice
Bibliographic record
Abstract
OBJECTIVE: While remission is possible in patients with ankylosing spondylitis (AS), it is often unclear what attitude should be adopted once remission has occurred. We investigated whether dosage adjustment is an effective means of maintaining remission. METHODS: This was a retrospective study drawn from clinical situations. Remission was defined using clinical measures [Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) ≤ 20/100 and no peripheral joint disease] and biological measures [C-reactive protein (CRP) levels ≤ normal value]. The tumor necrosis factor-α (TNF-α) inhibitors used were infliximab, adalimumab, and etanercept. Response predictors of remission were evaluated by logistic regression (age, CRP, HLA-B27 positivity, sex, duration of disease, and anti-TNF-α naivety). CRP and BASDAI were evaluated before and after dosage adjustment at about 6, 12, 24, and 36 months. RESULTS: One hundred eighty-nine patients with AS were included in the study, with a mean followup of 43.5 (± 17.9) months after the introduction of the first anti-TNF-α inhibitor. Mean age was 45.6 (± 12.5) years. Remission had occurred in 65 patients (35%). Significant response predictors of remission were male sex (p = 0.003) and anti-TNF-α naivety (p < 0.001). Dosage adjustment was observed 49 times, and progressively reducing treatment frequency was effective to maintain remission in a large number of patients for 36 months. The cumulative probability of continuing anti-TNF-α after dosage adjustment was 79.0% at 12 months, 70.5% at 24 months, and 58.8% at 36 months. CONCLUSION: Remission had occurred in 35% of the patients with AS under anti-TNF-α inhibitor therapy. Dosage adjustment and progressively reducing treatment frequency was effective in maintaining remission.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".