A semi-automated approach facilitated the assessment of the certainty of evidence in a network meta-analysis: Part 1 – Direct comparisons
Bibliographic record
Abstract
OBJECTIVES: To implement and evaluate a semi-automated approach to facilitate rating the Grading, Recommendation, Assessment, Development and Evaluation (GRADE) certainty of evidence (CoE) for direct comparisons within two living network meta-analysis. METHODS: For each of three GRADE domains (study limitations, indirectness, and inconsistency), decision rules were developed and used to generate automated judgments for each domain and the overall certainty. Inputs included risk of bias and indirectness ratings for each study and measures of heterogeneity. Indirectness ratings were made by two independent reviewers and resolved through consensus. With the help of an online tool (customized to our project), two independent raters viewed forest plots and additional data and could confirm or modify the suggested rating. Disagreements were resolved by consensus. We evaluated inter-rater reliability and accuracy. RESULTS: Across 374 direct comparisons, there was perfect agreement (100%) between the automated judgment and reviewer consensus, when only a single study was available (n = 292), and near-perfect agreement when more than one study was available (99%-100% for the three GRADE domains and 96% for overall rating). Inter-rater reliability was near perfect (Gwet's AC1 kappa score ranging from 96% to 100%). CONCLUSION: Automated judgments using established decision rules agreed with expert judgment for the vast majority of GRADE CoE ratings.
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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 | 0.154 | 0.441 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".