Harmonization of clinical practice guidelines for primary prevention and screening: actionable recommendations and resources for primary care
Bibliographic record
Abstract
Abstract Background Clinical practice guidelines (CPGs) synthesize high-quality information to support evidence-based clinical practice. In primary care, numerous CPGs must be integrated to address the needs of patients with multiple risks and conditions. The BETTER program aims to improve prevention and screening for cancer and chronic disease in primary care by synthesizing CPGs into integrated, actionable recommendations. We describe the process used to harmonize high-quality cancer and chronic disease prevention and screening (CCDPS) CPGs to update the BETTER program. Methods A review of CPG databases, repositories, and grey literature was conducted to identify international and Canadian (national and provincial) CPGs for CCDPS in adults 40–69 years of age across 19 topic areas: cancers, cardiovascular disease, chronic obstructive pulmonary disease, diabetes, hepatitis C, obesity, osteoporosis, depression, and associated risk factors (i.e., diet, physical activity, alcohol, cannabis, drug, tobacco, and vaping/e-cigarette use). CPGs published in English between 2016 and 2021, applicable to adults, and containing CCDPS recommendations were included. Guideline quality was assessed using the Appraisal of Guidelines for Research and Evaluation (AGREE) II tool and a three-step process involving patients, health policy, content experts, primary care providers, and researchers was used to identify and synthesize recommendations. Results We identified 51 international and Canadian CPGs and 22 guidelines developed by provincial organizations that provided relevant CCDPS recommendations. Clinical recommendations were extracted and reviewed for inclusion using the following criteria: 1) pertinence to primary prevention and screening, 2) relevance to adults ages 40–69, and 3) applicability to diverse primary care settings. Recommendations were synthesized and integrated into the BETTER toolkit alongside resources to support shared decision-making and care paths for the BETTER program. Conclusions Comprehensive care requires the ability to address a person’s overall health. An approach to identify high-quality clinical guidance to comprehensively address CCDPS is described. The process used to synthesize and harmonize implementable clinical recommendations may be useful to others wanting to integrate evidence across broad content areas to provide comprehensive care. The BETTER toolkit provides resources that clearly and succinctly present a breadth of clinical evidence that providers can use to assist with implementing CCDPS guidance in primary care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".