GRADE notes: How to use GRADE when there is “no” evidence? A case study of the expert evidence approach
Bibliographic record
Abstract
OBJECTIVES: One essential requirement of trustworthy guidelines is that they should be based on systematic reviews of the best available evidence. The GRADE Working Group has provided guidance for evaluating the certainty of evidence based on several domains. However, for many clinical questions, published evidence may be limited, too indirect or simply not exist. In this brief report (GRADE notes), we describe our method of developing evidence-based recommendations when publisheddirect evidence was lacking. STUDY DESIGN AND SETTING: When direct published literature was absent, an expert evidence survey was administered to panel members about their unpublished observations and case series. Focus was on collecting data about cases and outcome, not panel opinions. RESULTS: Out of 26 questions prioritized by the panel for pediatric venous thromboembolism, 12 had no, very limited, or very low certainty of evidence to inform them. The panel survey was administered for these questions. CONCLUSIONS: Areas of sparse evidence often reflect key questions that are critical to address in clinical practice guidelines due to the uncertainty among health care providers. The expert evidence approach used in this study is one method for panels totransparently deal with the lack of published evidence to directly inform recommendations.
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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 | 0.190 | 0.678 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.029 | 0.014 |
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".