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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.024 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".