Implementation of a clinical practice guideline in a primary care setting for the prevention and management of obesity in adults
Bibliographic record
Abstract
Over two-thirds of American adults have obesity or overweight, increasing the risk of comorbidities, mortality, and healthcare costs. Despite this growing issue, screening and counseling for an unhealthy weight are not common in primary care and clinical practice guidelines (CPGs) for prevention and management of obesity are underutilized. Following the stepwise approach outlined in the Registered Nurses' Association of Ontario Toolkit: Implementation of Best Practice Guidelines, the Institute for Clinical Systems Improvement: Prevention and Management of Obesity for Adults were implemented in a primary care office in Lexington, KY. Education was implemented with providers and staff. An assessment of readiness for change was completed at check-in and customizable phrases were built into the electronic health record. After a 12-week implementation, providers were consistently assessing for comorbidities, setting goals, and managing weight in those with obesity using evidence-based strategies. Readiness for change was being documented in less than 40% of those patients. For those with overweight providers were assessing readiness for change in only 30% of patients and were setting goals in just over 40% of patients. After the implementation, care more closely followed the CPG but additional steps are necessary to improve the prevention and management of obesity in this population.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".