Defining ranges for certainty ratings of diagnostic accuracy: a GRADE concept paper
Bibliographic record
Abstract
OBJECTIVE: The objective of the study was to clarify how the Grading of Recommendations Assessment, Development and Evaluation (GRADE) concept of certainty of evidence applies to certainty ratings of test accuracy. STUDY DESIGN AND SETTING: After initial brainstorming with GRADE Working Group members, we iteratively refined and clarified the approaches for defining ranges when assessing the certainty of evidence for test accuracy within a systematic review, health technology assessment, or guideline. RESULTS: Ranges can be defined both for single test accuracy and for comparative accuracy of multiple tests. For systematic reviews and health technology assessments, approaches for defining ranges include some that do not require value judgments regarding downstream health outcomes. Key challenges arise in the context of a guideline that requires ranges for sensitivity and specificity that are set considering possible effects on all critical outcomes. We illustrate possible approaches and provide an example from a systematic review of a direct comparison between two test strategies. CONCLUSIONS: This GRADE concept paper provides a framework for assessing, presenting, and making decisions based on the certainty of evidence for test accuracy. More empirical research is needed to support future GRADE guidance on how to best operationalize the candidate approaches.
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.
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 | 0.509 | 0.781 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.029 | 0.011 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.012 | 0.016 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".