144WS How to Make Judgements About the Quality or Strength of Evidence Transparent
Bibliographic record
Abstract
Background When assessing the confidence in intervention effects, i.e. the quality of evidence, guideline developers should make their judgement about this confidence transparent and provide an overall assessment (or grade) of the evidence (GIN & IOM standards 2011). The GRADE approach requires these judgments to be described in comments and footnotes. In a recent review of GRADE evidence summaries, we observed important variability in how guideline developers and authors of Cochrane systematic reviews perform these tasks. Objectives In this interactive workshop the participants will learn how to formulate understandable and informative reasons for down- and upgrading the quality of evidence by using a footnotes checklist. Target Group Systematic reviewers and guideline developers assessing the quality or strength of evidence. Description of the Workshop and of the Methods used to Facilitate Interactions We will present the development of the footnotes checklist. To get hands-on experience the participants will work in large and small groups to: 1) use the checklist on several examples of GRADE evidence profiles and 2) make a judgement about how informative these footnotes are, in particular with guideline panel meetings in mind. The examples will include challenging topics like evidence from single RCT and narrative reviews (no pooled estimates). The outcomes of these exercises will be discussed with the large group and will be used to further improve the checklist.
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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 | 0.650 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.070 | 0.057 |
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".