Involving patients in cardiovascular risk management with nurse-led clinics: a cluster randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Preventive guidelines on cardiovascular risk management recommend lifestyle changes. Support for lifestyle changes may be a useful task for practice nurses, but the effect of such interventions in primary prevention is not clear. We examined the effect of involving patients in nurse-led cardiovascular risk management on lifestyle adherence and cardiovascular risk. METHODS: We performed a cluster randomized controlled trial in 25 practices that included 615 patients. The intervention consisted of nurse-led cardiovascular risk management, including risk assessment, risk communication, a decision aid and adapted motivational interviewing. The control group received a minimal nurse-led intervention. The self-reported outcome measures at one year were smoking, alcohol use, diet and physical activity. Nurses assessed 10-year cardiovascular mortality risk after one year. RESULTS: There were no significant differences between the intervention groups. The effect of the intervention on the consumption of vegetables and physical activity was small, and some differences were only significant for subgroups. The effects of the intervention on the intake of fat, fruit and alcohol and smoking were not significant. We found no effect between the groups for cardiovascular 10-year risk. INTERPRETATION: Nurse-led risk communication, use of a decision aid and adapted motivational interviewing did not lead to relevant differences between the groups in terms of lifestyle changes or cardiovascular risk, despite significant within-group differences.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".