The Quality of Reporting Methods and Results in Network Meta-Analyses: An Overview of Reviews and Suggestions for Improvement
Bibliographic record
Abstract
INTRODUCTION: Some have suggested the quality of reporting of network meta-analyses (a technique used to synthesize information to compare multiple interventions) is sub-optimal. We sought to review information addressing this claim. OBJECTIVE: To conduct an overview of existing evaluations of quality of reporting in network meta-analyses and indirect treatment comparisons, and to compile a list of topics which may require detailed reporting guidance to enhance future reporting quality. METHODS: An electronic search of Medline and the Cochrane Registry of methodologic studies (January 2004-August 2013) was performed by an information specialist. Studies describing findings from quality of reporting assessments were sought. Screening of abstracts and full texts was performed by two team members. Descriptors related to all aspects of reporting a network meta-analysis were summarized. RESULTS: We included eight reports exploring the quality of reporting of network meta-analyses. From past reviews, authors found several aspects of network meta-analyses were inadequately reported, including primary information about literature searching, study selection, and risk of bias evaluations; statement of the underlying assumptions for network meta-analysis, as well as efforts to verify their validity; details of statistical models used for analyses (including information for both Bayesian and Frequentist approaches); completeness of reporting of findings; and approaches for summarizing probability measures as additional important considerations. CONCLUSIONS: While few studies were identified, several deficiencies in the current reporting of network meta-analyses were observed. These findings reinforce the need to develop reporting guidance for network meta-analyses. Findings from this review will be used to guide next steps in the development of reporting guidance for network meta-analysis in the format of an extension of the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analysis) Statement.
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 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 | 0.566 | 0.320 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".