GRADE: assessing the quality of evidence for diagnostic recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Making a diagnosis is the bread and butter of clinical practice, but in today’s world of many tests, the process has become complex. Guidelines for making an evidence-based diagnosis abound, but those making recommendations about diagnostic tests or test strategies must realise that clinicians require support to make diagnostic decisions that they can easily implement in daily practice. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group has developed a rigorous, transparent, and increasingly adopted approach for grading the quality of research evidence and strength of recommendations to guide clinical practice. This Notebook summarises GRADE’s process for developing recommendations for tests.1 Clinicians are trained to use tests for screening and diagnosis, identifying physiological derangements, establishing a prognosis, and monitoring illness and treatment response by assessing signs and symptoms, imaging, biochemistry, pathology, and psychological testing techniques.2 Sensitivity, specificity, positive predictive value, likelihood ratios, and diagnostic odds ratios are among the challenging terms that diagnostic studies typically deliver to clinicians, and all have to do with diagnostic accuracy. Not only do clinicians have difficulties remembering the definitions and calculations for these terms, these concepts are often complex to apply to individual patients. Many clinicians order a test despite uncertainty about how to interpret the result, and they also contribute to testing errors …
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.345 | 0.949 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 it