Guideline harmonization and implementation plan for the BETTER trial: Building on Existing Tools to Improve Chronic Disease Prevention and Screening in Family Practice
Bibliographic record
Abstract
BACKGROUND: The aim of the Building on Existing Tools to Improve Chronic Disease Prevention and Screening in Family Practice (BETTER) randomized controlled trial is to improve the primary prevention of and screening for multiple conditions (diabetes, cardiovascular disease, cancer) and some of the associated lifestyle factors (tobacco use, alcohol overuse, poor nutrition, physical inactivity). In this article, we describe how we harmonized the evidence-based clinical practice guideline recommendations and patient tools to determine the content for the BETTER trial. METHODS: We identified clinical practice guidelines and tools through a structured literature search; we included both indexed and grey literature. From these guidelines, recommendations were extracted and integrated into knowledge products and outcome measures for use in the BETTER trial. End-users (family physicians, nurse practitioners, nurses and dieticians) were engaged in reviewing the recommendations and tools, as well as tailoring the content to the needs of the BETTER trial and family practice. RESULTS: In total, 3-5 high-quality guidelines were identified for each condition; from these, we identified high-grade recommendations for the prevention of and screening for chronic disease. The guideline recommendations were limited by conflicting recommendations, vague wording and different taxonomies for strength of recommendation. There was a lack of quality evidence for manoeuvres to improve the uptake of guidelines among patients with depression. We developed the BETTER clinical algorithms for the implementation plan. Although it was difficult to identify high-quality tools, 180 tools of interest were identified. INTERPRETATION: The intervention for the BETTER trial was built by integrating existing guidelines and tools, and working with end-users throughout the process to increase the intervention's utility for practice. TRIAL REGISTRATION: ISRCTN07170460.
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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 | 0.498 | 0.638 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".