A semi-automated approach facilitated the assessment of the certainty of evidence in a network meta-analysis: Part 2 – Indirect and Mixed comparisons
Bibliographic record
Abstract
OBJECTIVES: To implement a semiautomated approach to facilitate rating the Grading, Recommendation, Assessment, Development and Evaluation certainty of evidence (CoE) for indirect and network meta-analysis (NMA) estimates. METHODS: We developed and implemented algorithms for generating automated ratings for the CoE for indirect and network estimates in two living NMAs of rheumatoid arthritis treatment. At the indirect stage, inputs included CoE ratings for direct estimates and the contribution matrix. Intransitivity ratings were assigned based on the indirectness ratings of the two direct estimates with the highest percent contribution. An online tool (customized to our project) facilitated assessment of imprecision on the network estimate. Automated ratings were reviewed by two independent experts. RESULTS: Across 1306 indirect comparisons, the contribution matrix identified the dominant branches of evidence regardless of whether a single first order loop was present (80%) or not. The reviewers agreed with all automated CoE ratings for incoherence (n = 34), network estimates (n = 34) and imprecision (n = 1447). They agreed with the automated intransitivity algorithm except when the total contribution of the top-two direct estimates was low (eg, <50%, which occurred in 38% of the estimates). CONCLUSION: Automated approaches facilitated CoE ratings for indirect and network estimates. Further work is required to define appropriate algorithms for intransitivity.
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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 | 0.296 | 0.607 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.017 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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; 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".