GRADE system: new paradigm
Bibliographic record
Abstract
PURPOSE OF REVIEW: An exposition of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to recommendations. RECENT FINDINGS: In this review, we outline the process whereby the strength of evidence from the literature undergoes a systematic reappraisal. The GRADE system allows four grades of evidence (high quality, moderate, low, and very low) and strength of recommendation is qualified as strong, weak, or conditional to an intervention (pro or con) and defined as the level of confidence that desirable effects predominate over untoward ones with a certain intervention. We provide research and clinical reviews in various settings in which this approach has been used. SUMMARY: Evidence-based medicine requires integrating the best available 'benchmark' literature with patient preferences and values (bedside) and is an evaluation process involving both patient and clinician, with a systematic assessment of the rated evidence from state-of-the-art medical literature. The GRADE methodology was developed as an application of evidence-based medicine to the field of recommendations and their formulation. The GRADE working group brings together clinical researchers and methodologists who developed a rating system to assess the quality of evidence for the purpose of making clinical practice recommendations.
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 | 0.125 | 0.375 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.030 | 0.021 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.019 | 0.014 |
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".