Patient perception of global cardiac risk and willingness to take preventative medicine
Bibliographic record
Abstract
Cardiovascular disease is a leading cause of death in North America. Patient compliance to medication is a necessity for successful preventative strategies. Patient preference or willingness to take preventative medications on the basis of individual cardiac risk assessment has not been assessed. The purpose of this study was compare patient-predicted cardiac risk (P-risk) to Framingham based risk (F-risk) within individuals and correlate risk with patient willingness to employ antihypertensive or lipid lowering therapies. Patients between the ages of 16 and 70 were recruited from three internist based atherosclerosis prevention/hypertension clinics over a six week period. A risk assessment survey was developed. F-risk was calculated within individual patients based on the Framingham formulas. Descriptive and statistical analyses were performed with SPSS. Fifty-two patients were interviewed with an average length of hypertension of 10.4 years (0.2–38 years). Average age was 52 (21–70). Eight patients were diabetic. Twenty patients surveyed had hypercholesterolemia. Blood pressure on presentation was 145/84 ± 20/13. Adverse reactions to medications had been experienced by 17 (32.7%) patients. Ten year P-risk of heart disease and stroke was 52% and 42% while F-risk was 14.2% and 9.5%, respectively. With improved blood pressure control, patients expected a 53% and 58% risk reduction for heart disease and stroke, respectively. Patient expectations of benefit from preventative medications were significantly higher than that reported in the medical literature. Patients were more likely to take an additional blood pressure medication than add/change their cholesterol medication. Likelihood of implementing further antihypertensive therapy was greater when perceived risks rather than Framingham calculated risks were presented to the patient (p=0.03, p<0.001). Likelihood of implementing further lipid lowering strategies was also greater when perceived risks were presented (p=0.02, p<0.001). We conclude that patient insight into global cardiac risk is limited. Patients tend to overestimate their individual risk and have high expectations of preventative therapy. Willingness to take additional therapy to decrease global cardiac risk declines when Framingham calculated risk and risk reduction from treatment of blood pressure and lipid levels are presented.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".