Leflunomide in rheumatoid arthritis: recommendations through a process of consensus
Bibliographic record
Abstract
OBJECTIVES: To determine, by consensus, the optimal use of leflunomide in rheumatoid arthritis (RA), using a multidisciplinary panel of experts and performing meta-analyses of available data. METHODS: A multidisciplinary panel of experts in RA was convened. Important questions, pertinent to the use of leflunomide in the treatment of RA, were defined by consensus at an initial meeting. Each question was allocated to subgroups of two or three members, who worked separately to prepare a balanced opinion, based on published literature, data from individual patients taking part in phase II and phase III clinical trials provided by Aventis, and data from a USA-based medical claims database (AETNA). The full group then reconvened to agree on an overall consensus statement. Recommendations concerning efficacy and tolerability versus comparator drugs and placebo were derived from two new meta-analyses. RESULTS: Leflunomide was at least as effective as sulphasalazine and methotrexate, and equally well tolerated on meta-analysis of trial data. Overall withdrawal rates for all adverse events were similar for all three drugs. Avoidance of the loading dose reduces 'nuisance' side-effects (e.g. nausea), but probably delays the onset of action. Adverse events could usually be managed by dose reduction and/or symptomatic therapy. CONCLUSIONS: On the basis of efficacy, safety and cost, leflunomide should be considered in patients with RA who have failed first-line DMARD drug therapy. In refractory cases, leflunomide may be used in combination with, for example, methotrexate before biological agents. Therapy should be initiated by a specialist, but repeat prescribing in general practice on a shared care basis is acceptable using agreed protocols. Clear mechanisms are required to monitor toxicity, with good communication between the patient and rheumatologist to manage nuisance side-effects and avoid unnecessary discontinuation of leflunomide.
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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 | 0.209 | 0.303 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".