Adalimumab in clinical practice. Outcome in 70 rheumatoid arthritis patients, including comparison of patients with and without previous anti-TNF exposure
Bibliographic record
Abstract
OBJECTIVE: To assess the efficacy and safety of the fully human recombinant monoclonal anti-TNF antibody adalimumab in routine clinical practice, including comparison of patients with and without previous anti-TNF exposure. METHODS: We prospectively studied the outcome of 70 rheumatoid arthritis patients treated with adalimumab in normal clinical practice. The primary outcome measures were Disease Activity Score 28 (DAS28), EULAR (European League Against Rheumatism) response and Health Assessment Questionaire (HAQ). RESULTS: Seventy-seven per cent achieved a EULAR response (26% good, 51% moderate) and 19% were in remission. The mean decrease in DAS28 was 2.1 (6.3-4.2; P<0.001). The mean decrease in HAQ score was 0.34 (2.07-1.73; P<0.001), 66% achieving a clinically significant decrease of greater than 0.22. Twenty-three per cent stopped treatment because of side-effects (7%) or failure to respond (16%). Of the 26 patients who had previously tried 29 biologicals, 65% responded to adalimumab. There was no significant difference in the change in mean DAS (P = 0.69) or HAQ (P = 0.88) between groups with and without previous anti-TNF exposure. Of the 13 patients with previous secondary failure to infliximab, 77% responded to adalimumab. Patients with previous secondary failure had significantly better improvement in DAS (P = 0.023) than patients with previous primary failure. CONCLUSION: Our clinical experience confirms that adalimumab is effective and safe in the treatment of RA. It also shows adalimumab is effective in patients with previous biological failures, particularly patients with secondary failure to infliximab.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".