Methods resources for authors new to conducting systematic reviews with network meta-analysis: a scoping review
Bibliographic record
Abstract
OBJECTIVES: To support systematic reviewers new to network meta-analysis (NMA), we (1) identified and described published methods resources for conducting systematic reviews (SRs) with NMA of randomized controlled trials (RCTs); (2) mapped the resources to the typical steps for conducting NMAs; and (3) identified NMA guidance gaps. STUDY DESIGN AND SETTING: We performed a scoping review and comprehensively searched major databases, gray literature sources, and websites for methods resources that described or informed any steps in conducting SRs with NMA to guide review authors, particularly those new to the method. Title, abstract, and full-text screening were conducted independently in duplicate using Covidence. NMA resources were narratively described and tabulated by guidance type, review steps, and topic and mapped to the steps of conducting a systematic review with NMA. RESULTS: We considered documents in the 2011-2025 date range and included 90; the majority (39%) were published between 2021 and 2025. Most were classified as guides/guidance (29%), methods/methodology (22%), or reviews (27%). We found that the rate of published guidance around most steps of NMA increased or remained stable over time. Most resources for software were guidance for R and Stata. Guidance documents on assumptions and certainty of evidence were abundant (in excess of 13 documents per topic), whereas fewer guidance documents were available on elements of protocol development and presentation of results. We mapped methods resources across steps in conducting SRs with NMA, identifying areas with sparse guidance. CONCLUSION: This scoping review provides a comprehensive reference for conducting SRs using NMA, especially for those new to the methods. It highlights the significant increase in guidance since 2011, particularly on evidence certainty and NMA assumptions, and the availability of user-friendly web tools. Future work should focus on advanced NMA guidance and decision tools to aid reviewers in further navigating NMA complexities.
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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 | 0.311 | 0.678 |
| Meta-epidemiology (narrow) | 0.007 | 0.010 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.098 | 0.072 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.011 | 0.020 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.257 | 0.076 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".