Knowing Your Risk Factors for Coronary Heart Disease Improves Adherence to Advice on Lifestyle Changes and Medication
Bibliographic record
Abstract
In Brief Implementation of guidelines for coronary heart disease prevention is less optimal in clinical practice. The aim of this study was to investigate if specific knowledge (patients' knowledge about their own coronary heart disease risk factors) would correlate to their adherence as measured by self-reported lifestyle changes, reaching defined treatment goals and adhering to treatment with prescribed drugs. The consecutive medical records of 509 men and women younger than 71 years, hospitalized for a cardiac event, were screened. Of these, 392 patients came for an interview and were subjected to a clinical examination. All patients received a questionnaire regarding their specific knowledge of risk factors and their adherence to lifestyle changes, which was completed by 347 patients. In addition, data were collected and analyzed on how their treatment goals were attained in 8 domains and their adherence to drug treatment. There were significant correlations between specific knowledge and self-reported lifestyle changes, the ability to reach treatment goals in all 8 domains, and adherence to prescribed drugs. Patients with coronary heart disease will benefit from increased specific knowledge of risk factors to adhere with lifestyle changes and prescribed medication after a cardiac event. Implementation of guidelines for coronary heart disease prevention is less optimal in clinical practice. The aim of this study was to investigate if specific knowledge (patients' knowledge about their own coronary heart disease risk factors) would correlate to their adherence as measured by self-reported lifestyle changes, reaching defined treatment goals and adhering to treatment with prescribed drugs. The consecutive medical records of 509 men and women younger than 71 years, hospitalized for a cardiac event, were screened. Of these, 392 patients came for an interview and were subjected to a clinical examination. All patients received a questionnaire regarding their specific knowledge of risk factors and their adherence to lifestyle changes, which was completed by 347 patients. In addition, data were collected and analyzed on how their treatment goals were attained in 8 domains and their adherence to drug treatment. There were significant correlations between specific knowledge and self-reported lifestyle changes, the ability to reach treatment goals in all 8 domains, and adherence to prescribed drugs. Patients with coronary heart disease will benefit from increased specific knowledge of risk factors to adhere with lifestyle changes and prescribed medication after a cardiac event. Article available at www.jcnjournal.com
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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 | 0.001 | 0.008 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".