GRADE approach to drawing conclusions from a network meta-analysis using a partially contextualised framework
Bibliographic record
Abstract
This article describes GRADE (grading of recommendations assessment, development and evaluation) guidance on how to make conclusions from a network meta-analysis of interventions that includes individual randomised controlled trials for one outcome at a time. The guidance is based on a partially contextualised approach in which review authors must establish ranges of magnitudes of effect that represent a trivial to no effect, small but important effect, moderate effect, and large effect. The principles guiding this framework are that interventions should be grouped in categories, based on the magnitude of the effect; and that the judgments that place interventions in such categories should consider the estimates of effect, the certainty of the evidence, and the rankings. We describe and illustrate the four steps of this framework using an example.
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 | 0.414 | 0.704 |
| Meta-epidemiology (narrow) | 0.008 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.046 | 0.023 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.013 | 0.016 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".