Grading quality of evidence and strength of recommendations in clinical practice guidelines: Part 2 of 3. The GRADE approach to grading quality of evidence about diagnostic tests and strategies
Bibliographic record
Abstract
The GRADE approach to grading the quality of evidence and strength of recommendations provides a comprehensive and transparent approach for developing clinical recommendations about using diagnostic tests or diagnostic strategies. Although grading the quality of evidence and strength of recommendations about using tests shares the logic of grading recommendations for treatment, it presents unique challenges. Guideline panels and clinicians should be alert to these special challenges when using the evidence about the accuracy of tests as the basis for clinical decisions. In the GRADE system, valid diagnostic accuracy studies can provide high quality evidence of test accuracy. However, such studies often provide only low quality evidence for the development of recommendations about diagnostic testing, as test accuracy is a surrogate for patient-important outcomes at best. Inferring from data on accuracy that using a test improves outcomes that are important to patients requires availability of an effective treatment, improved patients' wellbeing through prognostic information, or - by excluding an ominous diagnosis - reduction of anxiety and the opportunity for earlier search for an alternative diagnosis for which beneficial treatment can be available. Assessing the directness of evidence supporting the use of a diagnostic test requires judgments about the relationship between test results and patient-important consequences. Well-designed and conducted studies of allergy tests in parallel with efforts to evaluate allergy treatments critically will encourage improved guideline development for allergic diseases.
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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 | 0.145 | 0.497 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.040 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".