Comparative the efficacy and acceptability of immunosuppressive agents for myasthenia gravis: A protocol for systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Immunosuppressive drugs are routinely used to treat myasthenia gravis (MG). However, current recommendations provide limited evidence to support treatment options, leading to considerable variation in practice among healthcare specialists. Hence, we present a protocol for a systematic review and network meta-analysis (NMA) to update the evidence by comparing the efficacy and acceptability of oral immunosuppressive drugs for the treatment of MG. METHODS: We will conduct a systematic review and NMA of all randomized controlled trials evaluating the following oral immunosuppressive drugs for the treatment of MG. Published studies will be searched using the following databases from inception to November 23, 2021: CENTRAL, the CINAHL, MEDLINE, Embase, PsycINFO, Web of Science, and 3 Chinese databases (Chinese Biomedical Literatures Database, CNKI, and Wan Fang database). Assessment of study eligibility and data extraction will be conducted independently by 2 reviewers. The main outcome will be a quantitative MG scoring system. We will conduct Bayesian NMA to synthesize all evidence for each outcome and obtain a comprehensive ranking of all treatments. The quality of the evidence will be evaluated using the Grading of Recommendations, Assessment, Development, and Evaluations framework. RESULTS: The objective of this study was to assess the relative clinical efficacy and acceptability of first-line immunosuppressants for the treatment of MG, using a systematic review and NMA approach. CONCLUSION: In the absence of head-to-head trials comparing therapies, evidence from this NMA of available clinical trials will inform clinicians, patients, and families the risk-benefit profiles of different treatment options.
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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 | 0.101 | 0.159 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 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".