Disease Activity Cutoff Values in Initiating Tumor Necrosis Factor Inhibitor Therapy in Ankylosing Spondylitis: A German GO-NICE Study Subanalysis
Bibliographic record
Abstract
OBJECTIVE: International recommendations for the management of axial spondyloarthritis including ankylosing spondylitis (AS) recommend a Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) level of disease activity of ≥ 4 to initiate treatment with biologics. We aimed to evaluate the level of disease activity used to initiate tumor necrosis factor inhibitor (TNFi) treatment and the level of responses to treatment based on different BASDAI cutoffs. METHODS: This is a posthoc analysis of the noninterventional, prospective, GO-NICE study in the subgroup of biologic-naive AS treated with golimumab (GOL) 50 mg subcutaneously once monthly. RESULTS: Of the 244 biologic-naive AS patients at baseline, 70.5% had a BASDAI ≥ 4 (Group 1), 14.3% had 2.8 to < 4 (Group 2), and 15.2% had even < 2.8 (Group 3). A total of 134 patients (54.9%) completed the 24-month observational period. The mean BASDAI in Groups 1, 2, and 3 was initially 5.9 ± 1.3, 3.4 ± 0.4, and 2.0 ± 0.8, decreased to 2.2 ± 2.0, 1.9 ± 1.2, and 1.0 ± 1.2 within 3 months (all p < 0.0001 vs baseline), and decreased significantly to 2.2 ± 1.7, 1.9 ± 1.7, and 1.4 ± 1.0 at Month 24 (all p < 0.005), respectively. BASDAI 50% improvement was noted in 68.8%, 44.8%, and 45.2% of patients at Month 3, and in 84.9%, 61.9%, and 55.0% at Month 24. CONCLUSION: TNFi treatment was initiated in almost a third of AS patients with lower disease activity states as assessed by BASDAI cutoff of ≥ 4. Patients with a BASDAI between 2.8 and < 4 appeared to benefit significantly from GOL treatment, while patients with BASDAI < 2.8 did not. This finding should lead to a reevaluation of the established BASDAI cutoff of ≥ 4.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".