Applying Grading of Recommendations Assessment, Development and Evaluation (GRADE) to diagnostic tests was challenging but doable
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.
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How this classification was reachedexpand
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 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 | 0.506 | 0.810 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.040 | 0.022 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.013 | 0.011 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".