Grading quality of evidence and strength of recommendations in clinical practice guidelines
Bibliographic record
Abstract
The GRADE (Grades of Recommendation, Assessment, Development, and Evaluation) approach provides guidance to grading the quality of underlying evidence and the strength of recommendations in health care. The GRADE system's conceptual underpinnings allow for a detailed stepwise process that defines what role the quality of the available evidence plays in the development of health care recommendations. The merit of GRADE is not that it eliminates judgments or disagreements about evidence and recommendations, but rather that it makes them transparent. This first article in a three-part series describes the GRADE framework in relation to grading the quality of evidence about interventions based on examples from the field of allergy and asthma. In the GRADE system, the quality of evidence reflects the extent to which a guideline panel's confidence in an estimate of the effect is adequate to support a particular recommendation. The system classifies quality of evidence as high, moderate, low, or very low according to factors that include the study methodology, consistency and precision of the results, and directness of the evidence.
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 | 0.215 | 0.577 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.018 |
| Bibliometrics | 0.055 | 0.034 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".