Patientsʼ Understanding of Cardiac Risk Factors
Bibliographic record
Abstract
A 1-day point-prevalence study was conducted in our 141-bed tertiary cardiac care hospital in order to determine our patients' and their significant others' level of understanding of cardiac risk factors in general and of the patients' personal cardiac risk factors. There were 3 parts to the study: patient interviews, significant other (SO) interviews, and an audit of the participating patients' charts. Of the 87 patients who were able to participate, 71 completed the interviews as did 53 significant others. From recall, only 14 patients and 11 significant others were able to define what a cardiac risk factor was ("Habits or factors that contribute to heart disease") and they were unable to identify many general risk factors. However, when given a recognition task where cardiac risk factors were interspersed with sham factors, the overall mean general knowledge score was 13.6 for patients and 13.9 for significant others out of 16. The correlation between the patients' understanding of their cardiac risk factors and the significant others' understanding of them was reasonably good (r = 0.58, P < .0001), as was the correlation between the SOs' understanding and the charts (r = 0.58, P < .0001). There was less agreement between the patients' understanding and the chart documentation of cardiac risk factors (r = 0.36, P < .01). The findings of this study have implications for patient teaching as well as for documentation of cardiac risk factors.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".