Using disease risk estimates to guide risk factor interventions: field test of a patient workbook for self‐assessing coronary risk
Bibliographic record
Abstract
OBJECTIVE: To assess the feasibility and acceptability of a patient workbook for self-assessing coronary risk. DESIGN: Pilot study, with post-study physician and patient interviews. SETTING AND SUBJECTS: Twenty southern Ontario family doctors and 40 patients for whom they would have used the workbook under normal practice conditions. INTERVENTIONS: The study involved convening two sequential groups of family physicians: the first (n=10) attended focus group meetings to help develop the workbook (using algorithms from the Framingham Heart Study); the second (n=20) used the workbook in practice with 40 patients. Follow-up interviews were by interviewer-administered questionnaire. MAIN OUTCOMES MEASURES: Physicians' and patients' opinions of the workbook's format, content, helpfulness, feasibility, and potential for broad application, as well as patients' perceived 10-year risk of a coronary event measured before and after using the workbook. RESULTS: It took an average of 18 minutes of physician time to use the workbook: roughly 7 minutes to introduce it to patients, and about 11 minutes to discuss the results. Assessments of the workbook were generally favourable. Most patients were able to complete it on their own (78%), felt they had learned something (80%) and were willing to recommend it to someone else (98%). Similarly, 19 of 20 physicians found it helpful and would use it in practice with an average of 18% of their patients (range: 1-80%). The workbook helped to correct misperceptions patients had about their personal risk of a coronary event over the next 10 years (pre-workbook (mean (SD) %): 35.2 (16.9) vs. post-workbook: 17.3 (13.5), P < 0.0001; estimate according to algorithm: 10.6 (7.6)). CONCLUSIONS: Given a simple tool, patients can and will assess their own risk of CHD. Such tools could help inform otherwise healthy individuals that their risk is increased, allowing them to make more informed decisions about their behaviours and treatment.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.006 | 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".