Axial Spondyloarthritis Disease Activity Cutoffs at Golimumab Initiation in the French GO-PRACTICE Study: Parallels With the German GO-NICE Study
Bibliographic record
Abstract
Axial spondyloarthritis (axSpA) is a chronic inflammatory rheumatic disorder that includes both radiographic and nonradiographic entities1. International guidelines for axSpA management recommend nonsteroidal antiinflammatory drugs as the first line of treatment2,3. Initiation of antitumor necrosis factor-α (anti-TNF-α) therapy is recommended in patients with a Bath Ankylosing Spondylitis Disease Activity Index (BASDAI)4 of 4.0 and above, the cutoff signifying active disease2,5. Additionally, the influence of elevated baseline C-reactive protein (CRP) on major clinical response to anti-TNF-α treatment in ankylosing spondylitis (AS) has been demonstrated6 and the existing French guidelines for axSpA management do not recommend biologics for nonradiographic axSpA unless signs of inflammation are confirmed by magnetic resonance imaging or elevated CRP7. In the German GO-NICE study, the effectiveness of the anti-TNF-α golimumab (GOL) was assessed in 501 patients with AS over 2 years, using the BASDAI8. A posthoc analysis of 244 biologic-naïve patients with AS revealed that even those with baseline BASDAI as low as 2.8 responded significantly to GOL. Baseline CRP, however, had no effect on disease activity improvement9. Our objective is to supplement these findings by presenting a similar posthoc analysis from the French GO-PRACTICE study, the results of which provide further evidence to challenge the current BASDAI cutoff of 4.0 for anti-TNF-α initiation. GO-PRACTICE was a prospective, observational study performed in … Address correspondence to Dr. N. Harid, 10–12 Cours Michelet 92800 Puteaux, Île-de-France, France. Email: naoual.harid{at}msd.com.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".