Core GRADE 7: principles for moving from evidence to recommendations and decisions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it