Core GRADE 7: principles for moving from evidence to recommendations and decisions
Bibliographic record
Abstract
This seventh article in a seven part series presents the Core GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach for moving from evidence to recommendations or policy decisions. Core GRADE users make strong recommendations for an intervention versus a comparator when the desirable consequences clearly outweigh the undesirable consequences, and a conditional (weak) recommendation when the balance is less clear. Primary considerations in deciding on recommendations considering an individual patient perspective include balance of benefits, harms, and burdens; the certainty of evidence; and values and preferences. Secondary considerations, most important from a population perspective, include costs, feasibility, acceptability, and equity. Moving from evidence to recommendations begins with considering evidence regarding patients’ values and preferences and choosing the smallest difference in each outcome that patients perceive as important (the minimal important difference). Core GRADE users construct statements that make clear the values and preferences underlying their recommendations. In general, Core GRADE users make strong recommendations only when certainty of evidence is high or moderate. When evidence certainty is low, recommendations will be conditional under all but special circumstances.
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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 | 0.378 | 0.670 |
| Meta-epidemiology (narrow) | 0.005 | 0.008 |
| Meta-epidemiology (broad) | 0.010 | 0.019 |
| Bibliometrics | 0.030 | 0.018 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.031 | 0.011 |
| Open science | 0.025 | 0.021 |
| Research integrity | 0.029 | 0.037 |
| Insufficient payload (model declined to judge) | 0.026 | 0.024 |
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".