A for Effort: Learning From the Application of the GRADE Approach to Cancer Guideline Development
Bibliographic record
Abstract
Few would argue against a methodology that facilitates more explicit and transparent judgments about health care research evidence and the link to practice and policy decisions. In this issue of the JournalofClinicalOncology,DePalmaetal 1 describetheapplicationof one such method, the GRADE (Grades of Recommendation, Assessment, Development, and Evaluation) approach, for the development of clinical practice guidelines for breast, colorectal, and lung cancer treatment. The GRADE approach has emerged in response to concerns about the glut of competing grading systems, their limitations, and the confusion resulting from lack of a common rubric. GRADE (http://www.gradeworkinggroup.org/) provides an explicit method for arriving at recommendations classified according to the quality of supporting evidence. 2-8
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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 | 0.094 | 0.366 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.039 | 0.075 |
| Insufficient payload (model declined to judge) | 0.007 | 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".