Study protocol for developing the evaluation instrument of guideline adherence to GRADE approach (GRADE-Check)
Bibliographic record
Abstract
INTRODUCTION: Many clinical practice guidelines (CPGs) claim to use Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach, but its implementation varies. This suggests that CPG developers, methodologists and users would benefit from an instrument to evaluate the extent to which CPGs adhere to GRADE approach. Such a structured instrument is currently unavailable. Accordingly, this study will develop an evaluation instrument for assessing guideline adherence to the GRADE approach, which we have named 'GRADE-Check'. The goal is to target items to which CPGs fail to adhere and that potentially have serious consequences resulting in inaccuracies in certainty of evidence and inappropriate direction or strength of recommendations, thereby discriminating across CPGs in issues of importance. METHODS AND ANALYSIS: The panel will include up to 25 individuals with specific knowledge and expertise, including experienced authors, educators and methodologists on CPGs methodology and GRADE approach from relevant organisations. The instrument will focus on the key elements of GRADE, aiming for clarity for GRADE experts and non-GRADE experts to apply. The development process for GRADE-Check will consist of the following five phases: (1) recruitment of a panel of GRADE experts; (2) development of objectives and scope for the development of GRADE-Check and criteria for item selection; (3) generation of candidate items through a literature review and panel consultation; (4) panellist discussion to construct the initial draft and extended explanation manual and (5) user testing. ETHICS AND DISSEMINATION: This study has been approved by the Medical Ethics Committee of Zhongnan Hospital of Wuhan University (no. (2025047K)). Our research findings will be published in peer-reviewed journal articles and presented at academic conferences. Additionally, the dissemination plan will include considerations for the development of implementation manuals, a dedicated project website and training tools.
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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 | 0.110 | 0.175 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.136 | 0.037 |
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".