Comparative efficacy and safety of current therapies for early rheumatoid arthritis: a systematic literature review and network meta-analysis.
Bibliographic record
Abstract
OBJECTIVES: This systematic literature review (SLR) and network meta-analysis (NMA) was aimed at comparing the relative efficacy and safety of abatacept (ABA) with other currently recommended therapies for patients with early RA. METHODS: An SLR (January 1998 to June 2018) was conducted including MEDLINE®, Embase, and CENTRAL databases, and grey literature. Population was adults with active RA for ≤2 years treated with biologic DMARDs as monotherapy or in combination with conventional DMARDs. A Bayesian NMA was performed using randomised controlled trials (RCTs) and comparisons for ACR50, DAS28 remission, withdrawal due to adverse events and total withdrawal where reported. RESULTS: Ninety publications pertaining to 69 studies (43 RCTs and 26 observational studies) were identified. Twenty-eight RCTs were eligible to be included in the NMA. ABA as monotherapy was similar to the combination of ABA+methotrexate (MTX) for ACR50 (RR: 0.82 [95% CI 0.51-1.35]), and DAS28 remission (RR: 0.69 [95% CI 0.37-1.3]), as well as for withdrawal due to AEs (RR: 2.35 [95% CI 0.69-7.38]) and all-cause withdrawal (RR: 1.73 [95% CI 0.905-3.35]). ABA as monotherapy and ABA+MTX were both comparable to all other therapies for the main efficacy and safety outcomes. Observational study data reported was congruous with the RCT analysis. CONCLUSIONS: The results of this NMA show similar efficacy and safety between ABA (as monotherapy or in combination with MTX) and other biologics in early RA. Further comparison of different treatment options for early RA is warranted as growing research provides evidence for the application of new novel therapies for 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 | 0.027 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".