Understanding network meta-analysis methodology for the ophthalmologist
Bibliographic record
Abstract
PURPOSE OF REVIEW: Over the past decade, the number of studies published using network meta-analyses (NMAs) has rapidly increased, and there have been continued advancements to further advance this analysis approach. Due to the fast moving and changing landscape in the infancy of NMA methodology, there is a lack of consistency and standardization for this approach. This article aims to summarize the crucial components of an NMA for both future readers, and for potential NMA authors. RECENT FINDINGS: Key components of NMAs include, but are not limited to, reporting the proposed analysis methods, assessment of risk of bias within the included studies, reporting the overall quality of the available evidence, and defining the parameters in which the results will be presented. Although NMA allows for a comprehensive evaluation of all available treatment options for a given condition, we believe that there is importance in ensuring clear understanding and appropriate interpretation of results to inform clinical practice. SUMMARY: While many components of NMA mirror those of traditional pairwise meta-analysis, there are many novel methodologies that are specific to this approach. It is imperative that future NMAs follow guidance from key methodology groups, as these provide valuable tools for conducting and reporting NMAs.
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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 | 0.137 | 0.425 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".