Core GRADE 1: overview of the Core GRADE approach
Bibliographic record
Abstract
This first article in a seven part series presents an overview of the essential elements of the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach that has proved extremely useful in systematic reviews, health technology assessment reports, and clinical practice guidelines. GRADE guidance has appeared in many articles dealing with both core issues and more specialised and complex guidance, and it has evolved over time. This series of articles presents GRADE essentials, Core GRADE, focusing on the core judgments necessary to summarise the comparative evidence about alternative care options and to make recommendations that apply to the care of individual patients. This article presents detailed guidance on formulating questions using the PICO (population, intervention, comparison, outcome) structure, and refining the question considering possible differences in relative and absolute effects across patient groups. The article then provides an overview of the remainder of the Core GRADE approach, including decisions about the certainty of the evidence and considerations in moving from evidence to guidance and 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 | 0.180 | 0.504 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.029 | 0.022 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.011 | 0.012 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.026 | 0.018 |
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".