Two decades of network meta‐analysis: Roadmap to their applications and challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently, Ades and colleagues discussed the controversies and advancements in network meta-analysis (NMA) over the past two decades, discussing its reliability, assumptions, novel approaches, and provided some useful recommendations for the conduction of NMAs. The present discussion paper builds on the insights by Ades and colleagues, providing a roadmap for NMA applications, advancements in software and tools, and approaches designed to facilitate the assessment and interpretation of NMA findings. It also discusses the impact of NMA across disciplines, particularly for policymakers and guideline developers. Despite 20 years of NMA history, challenges remain in understanding and assessing assumptions, communicating and interpreting findings, and applying common approaches like network meta-regression and NMA involving non-randomized studies in readily available software. NMA has proven particularly valuable in clinical decision-making, which highlights the need for additional training and interdisciplinary collaboration of knowledge users, including patient engagement, to enhance its adoption and address real-world problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.508 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 it