Comparing Tapering Strategy to Standard Dosing Regimen of Tumor Necrosis Factor Inhibitors in Patients with Spondyloarthritis in Low Disease Activity
Bibliographic record
Abstract
OBJECTIVE: To compare clinical outcomes, incidence of flares, and administered drug reduction between patients with spondyloarthritis (SpA) under TNF inhibitor (TNFi) tapering strategy with patients receiving a standard regimen. METHODS: In this retrospective study, 74 patients with SpA from Spain on tapering strategy (tapering group; TG) were compared with 43 patients from the Netherlands receiving a standard regimen (control group; CG). The Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) was measured at visit 0 (prior to starting the TNFi), visit 1 (prior to starting tapering strategy in TG and at least 6 months with BASDAI < 4 after starting the TNFi in the TG and CG), visit 2 (6 mos after visit 1), visit 3 (1 year after visit 1), and visit 4 (the last visit available after visit 1). RESULTS: An overall reduction of the administered drug was seen at visit 4 in the TG [dose reduction of 22% for infliximab (IFX) and an interval elongation of 28.7% for IFX, 45.2% for adalimumab, and 51.5% for etanercept] without significant differences in the BASDAI between the groups at visit 4 (2.15 ± 1.55 in TG vs 2.11 ± 1.31 in CG, p = 0.883). The number of patients with flares was similar in both groups [22/74 (30%) in the TG vs 8/43 (19%) in the CG, p = 0.184]. CONCLUSION: The tapering strategy in SpA results in an important reduction of the drug administered, and the disease control remains similar to that of the patients with SpA receiving the standard regimen.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".