A Novel Approach to Cardiovascular Health By Optimizing Risk Management (ANCHOR): Behavioural Modification in Primary Care Effectively Reduces Global Risk
Bibliographic record
Abstract
BACKGROUND: Primary care is well positioned to facilitate cardiovascular risk improvement and reduce future cardiovascular disease (CVD) burden. METHODS: The efficacy of risk factor screening, behavioural counselling, and pharmacological treatment to lower CVD risk was assessed via a prospective pre- and postintervention health risk assessment, individualized intervention with behaviour modification, risk factor treatment, and linkage to community programs, with 1-year follow-up and final health risk assessment. Primary outcome was the proportion of subjects with moderate and high baseline Framingham Risk Score (FRS) reducing their risk by 10% and 25%, respectively; the secondary end point was the proportion dropping ≥ 1 risk category. RESULTS: Patients were enrolled (N = 1509) from March 2006 through October 2008 and 72% completed the study. This analysis focuses on 563 subjects with moderate or high baseline FRS, and excluded 325 low-risk patients and 205 with established CVD or diabetes mellitus. Median age was 56 years, 57.7% were female. The primary outcome was achieved in 31.8% (N = 112; 95% confidence interval [CI], 26.9%-36.6%) of moderate risk FRS participants and 47.9% (N = 101; 95% CI, 41.2%-54.6%) of high-risk participants. The secondary outcome was achieved by 37.2% (N = 210; 95% CI, 33.2%-41.2%). Prevalence of metabolic syndrome fell from 79.2% (N = 446; 95% CI, 75.9%-82.6%) at entry to 52.8% (N = 303; 95% CI, 48.7%-56.9%) at study end. Significant improvements in all modifiable risk factors occurred through lifestyle modification. CONCLUSIONS: Global cardiovascular risk can be effectively decreased via lifestyle changes informed by readiness to change assessment and individualized counselling targeting specific behaviours. TRIAL REGISTRATION: ClinicalTrials.gov number NCT01620996.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".