Predicting patient knowledge of cardiac risk factors: A comparison of two approaches
Bibliographic record
Abstract
The University of Ottawa Heart Institute conducted a survey of patients to understand the level of knowledge of cardiac risk factors and to identify any subgroups of patients that could benefit from specially designed educational programs. This thesis compares two approaches to the analysis of this multidimensional dataset. Both techniques looked at the 10 modifiable risk factors and a number of predictor variables (age, gender, education, and smoking status). Logistic regression was hampered by low sample size, sparse data, and the high probability responses of many of the binary knowledge variables. Only one risk factor was successfully explained by any of the predictor variables. Correspondence analysis demonstrated that those who are unaware of smoking as a risk factor are not current smokers; knowledge of low fibre diet is related to education but not to gender; females are more aware of high salt diet and stress as risk factors, and are more likely to have never smoked; smokers tend to have lower education and be unaware of the risk of a low fibre diet.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".