Applying Grading of Recommendations Assessment, Development and Evaluation (GRADE) to diagnostic tests was challenging but doable
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
OBJECTIVES: The Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group developed an approach to assess the quality of evidence of diagnostic tests. Its use in Cochrane diagnostic test accuracy reviews is new. We applied this approach to three Cochrane reviews with the aim of better understanding the application of the GRADE criteria to such reviews. STUDY DESIGN AND SETTING: We selected reviews to achieve clinical and methodological diversities. At least three assessors independently assessed each review according to the GRADE criteria of risk of bias, indirectness, imprecision, inconsistency, and publication bias. Two teleconferences were held to share experiences. RESULTS: For the interpretation of the GRADE criteria, it made a difference whether assessors looked at the evidence from a patient-important outcome perspective or from a test accuracy standpoint. GRADE criteria such as inconsistency, imprecision, and publication bias were challenging to apply as was the assessment of comparative test accuracy reviews. CONCLUSION: The perspective from which evidence is graded can influence judgments about quality. Guidance on application of GRADE to comparative test reviews and on the GRADE criteria of inconsistency, imprecision, and publication bias will facilitate the operationalization of GRADE for diagnostics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.598 | 0.760 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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