Bibliographic record
Abstract
GRADE is a globally recognized approach for assessing the certainty of evidence (also: quality or certainty of evidence) and formulating recommendations in systematic reviews, guidelines and other evidence syntheses. GRADE (Grading of Recommendations Assessment, Development and Evaluation) was introduced approximately 20 years ago by an international working group led by Canadian physician Gordon Guyatt, one of the pioneers of evidence-based practice [ 1 ]. GRADE has evolved continuously over time and has now reached a high level of complexity. In the context of the strengths and weaknesses of the established GRADE concept, the Core GRADE approach recently presented by Guyatt et al. represents a significant development, which should lead to simpler and more frequent use of GRADE [ 2 ]. The new Core GRADE approach is described in detail in a 7-part series of articles and is highly relevant to physiotherapy science [ 2 ]. Publication History Article published online: 08 December 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
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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 | 0.114 | 0.500 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.021 |
| Bibliometrics | 0.051 | 0.028 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.013 | 0.008 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.147 | 0.041 |
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".