Impact and use of reviews and ‘overviews of reviews’ to inform clinical practice guideline recommendations: protocol for a methods study
Bibliographic record
Abstract
INTRODUCTION: Guidelines are systematically developed recommendations to assist practitioner and patient decisions about treatments for clinical conditions. High quality and comprehensive systematic reviews and 'overviews of systematic reviews' (overviews) represent the best available evidence. Many guideline developers, such as the WHO and the Australian National Health and Medical Research Council, recommend the use of these research syntheses to underpin guideline recommendations. We aim to evaluate the impact and use of systematic reviews with and without pairwise meta-analysis or network meta-analyses (NMAs) and overviews in clinical practice guideline (CPG) recommendations. METHODS AND ANALYSIS: CPGs will be retrieved from Turning Research Into Practice and Epistemonikos (2017-2018). The retrieved citations will be sorted randomly and then screened sequentially by two independent reviewers until 50 CPGs have been identified. We will include CPGs that provide at least two explicit recommendations for the management of any clinical condition. We will assess whether reviews or overviews were cited in a recommendation as part of the development process for guidelines. Data extraction will be done independently by two authors and compared. We will assess the risk of bias by examining how each guideline developed clinical recommendations. We will calculate the number and frequency of citations of reviews with or without pairwise meta-analysis, reviews with NMAs and overviews, and whether they were systematically or non-systematically developed. Results will be described, tabulated and categorised based on review type (reviews or overviews). CPGs reporting the use of the Grading of Recommendations, Assessment, Development and Evaluation approach will be compared with those using a different system, and pharmacological versus non-pharmacological CPGs will be compared. ETHICS AND DISSEMINATION: No ethics approval is required. We will present at the Cochrane Colloquium and the Guidelines International Network conference.
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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 | 0.191 | 0.333 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.017 | 0.021 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.094 | 0.019 |
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".