The Reason for Discontinuation of the First Tumor Necrosis Factor (TNF) Blocking Agent Does Not Influence the Effect of a Second TNF Blocking Agent in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To investigate whether the reason for discontinuation of the first tumor necrosis factor (TNF) blocking agent influences the effect of a second TNF blocking agent. METHODS: Data were used from 2 Dutch registries including patients with rheumatoid arthritis (RA) treated with TNF blocking agents. Patients were divided into 3 groups based on reason for discontinuation of the first: nonresponse, loss of response, or adverse events. The primary outcome was the change from baseline of the disease activity (by DAS28) at 6 months, corrected for the baseline DAS28 score. Secondary outcomes were the change from baseline at 3 months, EULAR response rates, and the percentages of patients who reached a DAS28 score < or = 3.2 at 3 and at 6 months. RESULTS: In total, 49 patients who failed due to nonresponse, 75 due to loss of response, and 73 due to adverse events were included. At 6 months, the change of DAS28 score from baseline did not differ significantly between the groups (-0.6 to -1.3; p > or = 0.173) and similar good and moderate response rates were found (12% to 18%, p > or = 0.523, and 34% to 55%, p > or = 0.078, respectively). The secondary outcomes were also comparable between the 3 groups. CONCLUSION: The results of our observational study suggest that a second TNF blocking agent may be effective after failure of the first, regardless of the reason for discontinuation of the first TNF blocking agent.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".