Addressing Challenges When Applying GRADE to Public Health Guidelines: A Scoping Review Protocol and Pilot Analysis
Bibliographic record
Abstract
This is a protocol for a scoping review that aims to determine how guideline authors using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach have addressed previously identified challenges related to public health. The Joanna Briggs Institute (JBI) methodology for scoping reviews will be followed. We will search and screen titles of guidelines for all languages published in 2013-2021 in: the GIN library, BIGG database, Epistemonikos GRADE guidelines repository, GRADEpro Database, MAGICapp, NICE and WHO websites. Two reviewers will independently screen full texts of the documents identified. The following information will be extracted: methods used for identifying different stakeholders and incorporating their perspectives; methods for identification and prioritization of non-health outcomes; methods for determining thresholds for decision-making; methods for incorporating and grading evidence from non-randomized studies; methods for addressing concerns with conditional recommendations in public health; methods for reaching consensus; additional methodological concerns; and any modifications made to GRADE. A combination of directed content analysis and descriptive statistics will be used for data analysis, and the findings presented narratively in a tabular and graphical form. In this protocol, we present the pilot results from 13 identified eligible guidelines issued between January and August 2021. We will publish the full review results when they become available.
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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 | 0.392 | 0.439 |
| Meta-epidemiology (narrow) | 0.004 | 0.008 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.081 | 0.031 |
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".