Updating the PRISMA reporting guideline for network meta-analysis: a scoping review
Bibliographic record
Abstract
STANDFIRST This scoping review represents the initial step in updating the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) extension for network meta-analysis (NMA) to improve the usability and relevance of systematic reviews with NMA for diverse audiences. The update will address gaps in reporting, such as documenting methods for assessing NMA homogeneity and transitivity, defining intervention nodes, and considering advances in statistical modeling. It will align with PRISMA 2020 and incorporate input from diverse knowledge users, including patients and the public. This scoping review included 61 studies, comprising 23 guidance documents and 38 overviews of reviews evaluating the completeness of reporting and methodological quality of published NMAs. We identified 37 additional NMA items for inclusion in the next step—the Delphi survey. Building on a 2014 scoping review by our team, we incorporated recent studies to inform the PRISMA-NMA update, adhering to established standards for guideline development. SUMMARY POINTS Network meta-analyses (NMAs) are increasingly prominent in evidence synthesis, but their methodological transparency and reporting completeness remain inconsistent, which affects the reliability of results. This scoping review highlights the need to update PRISMA-NMA to address evolving methodologies (e.g., component NMA) and reporting challenges to improve accessibility and utility of NMA findings. The review underscores progress and gaps in NMA reporting since the 2015 PRISMA-NMA extension, focusing on synthesis methods, homogeneity, consistency and transitivity assessments, and network geometry. Key recommendations, such as defining pre-specified nodes, specifying statistical methods, and addressing competing interests, were often overlooked in NMA reporting, but addressing these gaps could enhance transparency, reproducibility, and trust in NMA results.
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.
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 | 0.553 | 0.788 |
| Meta-epidemiology (narrow) | 0.005 | 0.010 |
| Meta-epidemiology (broad) | 0.014 | 0.041 |
| Bibliometrics | 0.030 | 0.032 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.012 | 0.014 |
| Research integrity | 0.013 | 0.021 |
| Insufficient payload (model declined to judge) | 0.032 | 0.017 |
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".