Anti-infliximab antibodies in patients with rheumatoid arthritis who require higher doses of infliximab to achieve or maintain a clinical response.
Bibliographic record
Abstract
OBJECTIVE: To determine whether the need to use doses of infliximab greater than 3 mg/kg every 8 weeks to achieve or maintain clinical response in patients with rheumatoid arthritis (RA) is associated with differences in baseline clinical characteristics or anti-infliximab antibodies. METHODS: Baseline clinical characteristics and anti-infliximab levels were evaluated retrospectively in a cohort of 51 consecutive patients with RA treated with infliximab at a single center. Patients were divided into 2 groups for comparison: Group 1 patients achieved and maintained clinical responses with infliximab 3 mg/kg every 8 weeks; Group 2 patients required higher doses. RESULTS: Thirty-two (63%) patients required infliximab dose escalation (Group 2). There were no statistically significant differences in baseline or clinical characteristics between Group 1 and Group 2 patients. Anti-infliximab antibodies occurred in 47% of Group 2 versus 27% of Group 1 patients, with higher anti-infliximab antibody concentrations in Group 2 patients (mean +/- SD: 18.3 +/- 8.9 g/ml vs 7.5 +/- 4.8 g/ml; p = 0.02). Patients who developed anti-infliximab antibodies were younger and receiving less prednisone at the time of infliximab initiation than patients who did not. CONCLUSION: Finding higher anti-infliximab antibody concentrations in patients who needed dose escalation of infliximab to achieve or maintain clinical responses with lower serum trough levels of infliximab suggests that development of anti-infliximab antibodies may reduce clinical efficacy of infliximab in some patients with RA.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".