Does the Type of Failure and the Choice of the Second Biologic Influence Response and Persistence on Medication in Rheumatoid Arthritis?
Bibliographic record
Abstract
BACKGROUND: The type of failure may predict response to a second biologic. We evaluated the response to a second tumor necrosis factor inhibitor (TNFi) or non-TNFi in patients failing their initial TNFi, either primarily or secondarily. METHODS: Patients with rheumatoid arthritis who were biologic-naive and had a Clinical Disease Activity Index (CDAI) >10, who started their first TNFi for ≥3 months and then switched to a second biologic, were included in the study. Secondary failure was defined as 2 consecutive low-CDAI visits and then switching to a second biologic while they had moderate/severe CDAI. Primary failure was defined if it did not meet the definition of secondary failure, or if they had at least 1 moderate/severe CDAI after 3 months on treatment. We used multivariable logistic regression comparing primary versus secondary failure for achievement of CDAI ≤10 (primary outcome) and minimal clinically important differences (secondary outcome) at 6 months after switch. RESULTS: Of the 462 patients included, 64.3% and 35.7% stopped the first TNFi because of a primary and secondary failure, respectively. Patients with primary failure had a more severe disease (CDAI mean, 26.39 vs. 21.61; p < 0.001). The likelihood of achieving CDAI ≤10 (odds ratio, 4.367; 95% confidence interval, 2.428-7.856) and minimal clinically important difference (odds ratio, 2.851; 95% confidence interval, 1.619-5.020) was significantly higher for secondary than primary failure regardless of choice of a second agent. CONCLUSION: Patients with rheumatoid arthritis with secondary failure to a first TNFi responded better to a second biologic agent, regardless of the choice of biologic.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".