A guide and pragmatic considerations for applying GRADE to network meta-analysis
Bibliographic record
Abstract
Assessing the certainty of evidence from network meta-analyses using the GRADE (grading of recommendations, assessment, development, and evaluations) approach requires not only a thorough understanding of the methods but also substantial workload for raters. This article describes how implementing practical strategies (including rule setting and automation) can facilitate efficient application of the GRADE approach to rating certainty of evidence in network meta-analyses while maintaining rigor. This article describes a stepwise strategy for proceeding through the process, including assessment of the certainty of direct, indirect, and NMA estimates; developing directions for each step in the process; implementation of rules to improve robustness of information appraisal to reduce workload; and alternatives for automation of some of the required steps. The presented approach includes a detailed description of every step of the process supported by figures, tables, and real life examples. To facilitate implementation of this process, a spreadsheet that incorporates automation of several of the steps described is also provided (https://www.covid19lnma.com/).
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 | 0.313 | 0.686 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.013 | 0.027 |
| Insufficient payload (model declined to judge) | 0.030 | 0.020 |
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".