The efficacy of inhibiting tumour necrosis factor and interleukin 1 in patients with rheumatoid arthritis: a meta-analysis and adjusted indirect comparisons
Bibliographic record
Abstract
OBJECTIVE: New treatments that inhibit the cytokines tumour necrosis factor alpha (TNFalpha) and interleukin 1 (IL-1) in the treatment of rheumatoid arthritis have proven clinical effect against placebo and methotrexate (MTX) in several clinical trials in early and late-stage disease and different severity groups. Since there are no head-to-head randomized controlled trials directly comparing the currently available treatments, etanercept, adalimumab, infliximab or anakinra, we perform a meta-analysis that adjusts for differences between study characteristics, and allows indirect comparisons between treatments. METHODS: Thirteen trials of cytokine antagonists were included from a systematic review of the literature. They reported the primary outcome of American College of Rheumatology (ACR) response criteria at 6 months or beyond. Meta-analytical methods are used to quantify relative treatment effects, using the log odds ratio of an ACR20 or ACR50 response at 6 months, whilst adjusting for study-level variables. RESULTS: In each of the trials, cytokine treatment was efficacious in comparison with placebo or MTX. For each treatment, the inclusion of MTX in combination improved the response. After adjustment for study-level variables, we found TNFalpha antagonists to be more efficacious compared with anakinra (P < 0.05). Indirect comparisons between the three TNFalpha antagonists indicated no difference in efficacy. Sensitivity analysis using a different statistical model structure confirmed these results. CONCLUSION: When the outcome of interest is the probability of an ACR20 or ACR50 response at 6 months we found: (i) treatment with the IL-1 antagonist anakinra is better than placebo; (ii) for each treatment, the use of combination MTX improves the probability of response; (iii) treatment with any of the TNFalpha antagonists is better than with the IL-1 antagonist anakinra; and (iv) all drugs in the TNFalpha antagonist class are no different from each other.
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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 | 0.029 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.066 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".