GRADE approach to drawing conclusions from a network meta-analysis using a minimally contextualised framework
Bibliographic record
Abstract
This article describes GRADE (grading of recommendations assessment, development and evaluation) guidance on how to draw conclusions from a network meta-analysis of interventions that includes individual randomised controlled trials addressing a single outcome. The guidance uses a minimally contextualised approach that avoids value judgments regarding the magnitude of intervention effects. The framework is based on two principles: interventions should be grouped in categories, from the most to the least effective or harmful; and the judgments that place interventions in such categories should simultaneously consider the estimates of effect, the certainty of the evidence, and the rankings. The framework includes five steps, which we describe and illustrate using an example. The framework is simple, methodologically sound, and flexible, allowing for modifications to resolve situations in which additional complexity or value judgments might be appropriate.
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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 | 0.393 | 0.774 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.030 | 0.014 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.013 | 0.014 |
| Research integrity | 0.013 | 0.026 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".