Updating the PRISMA reporting guideline for network meta-analysis: a scoping review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: This scoping review is the first step in the process of updating the 2015 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for network meta-analysis (NMA). It builds up on a 2014 scoping review by our team and aims to enhance the usability, completeness, and transparency of NMA reporting for diverse audiences, including patients and the public. The updated extension will align with PRISMA 2020 and will address gaps in reporting, such as documenting methods for assessing NMA homogeneity and transitivity, defining intervention nodes in a network of studies, and considering advances in statistical modeling with NMA. METHODS: We registered the study protocol with the Open Science Framework and published it in Joanna Briggs Institute (JBI) Evidence Synthesis. We searched multiple databases and gray literature sources, and screened studies in duplicate. Data extraction was also conducted in duplicate using a standardized form, focusing on study characteristics, authors' reporting recommendations, and proposed additions to PRISMA-NMA and/or PRISMA 2020. RESULTS: Sixty-one studies met eligibility criteria, including 23 guidance documents and 38 overviews of reviews assessing the completeness or quality of NMA reporting. We identified 37 additional reporting items relevant to NMAs, which will inform the next stage of the PRISMA-NMA update (a Delphi consensus process). CONCLUSION: Our findings support the urgent need to update the PRISMA-NMA guideline. Addressing persistent reporting gaps and incorporating recent methodological developments is critical to improving the transparency, reproducibility, and trustworthiness of NMAs.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.667 | 0.835 |
| Meta-epidemiology (narrow) | 0.005 | 0.009 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.035 | 0.036 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.018 | 0.014 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".