Introduction to network meta-analysis: understanding what it is, how it is done, and how it can be used for decision-making
Bibliographic record
Abstract
Network meta-analysis (NMA), a statistical technique that allows systematic reviewers to simultaneously compare more than 2 alternatives, makes use of indirect evidence from studies comparing interventions of interest to a common comparator. The capacity for multiple simultaneous comparisons makes NMA appealing for evidence-based decision-makers. This article, aimed at users of systematic reviews (SRs) with NMAs and at those who are considering conducting SRs with NMAs, provides an introductory level overview of this topic. We describe the main considerations that those conducting systematic reviews with NMA should bear in mind, including decisions regarding grouping interventions into analysis nodes, and testing the assumptions that assure the validity of NMA. We explain and illustrate how both systematic reviewers and users should draw conclusions from NMA that are appropriate and useful for decision-making. Finally, we provide a list of tools that facilitate the conduct and interpretation of NMAs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.195 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads 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".