Grading Evidence for Laboratory Test Studies Beyond Diagnostic Accuracy: Application to Prognostic Testing.
Bibliographic record
Abstract
BACKGROUND: Evidence-based guideline development requires transparent methodology for gathering, synthesizing and grading the quality and strength of evidence behind recommendations. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) project has addressed diagnostic test use in many of their publications. Most of the work has been directed at diagnostic tests and no consensus has been reached for prognostic biomarkers. AIM OF THIS PAPER: The GRADE system for rating the quality of evidence and the strength of a recommendation is described. The application of GRADE to diagnostic testing is discussed and a description of application to prognostic testing is detailed. Some strengths and limitations of the GRADE process in relation to clinical laboratory testing are presented. CONCLUSIONS: The GRADE system is applicable to clinical laboratory testing and if correctly applied should improve the reporting of recommendations for clinical laboratory tests by standardising the style of recommendation and by encouraging transparent reporting of the actual guideline process.
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 | 0.234 | 0.646 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.033 | 0.021 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| 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".