Personalized Patient Education for Cardiovascular Risk Management: A Synergy of Behavioural Modelling, SCORE and Compositional Information Personalization Methods
Bibliographic record
Abstract
Personalized patient educational programs offer the opportunity to empower patients to self-manage Cardiovascular Disease (CVD) risk. In this paper, we present the PULSE project - Personalization Using Linkages of SCORE and behaviour change readiness to web-based Education - that provides personalized patient education for CVD risk management. Our personalization approach leverages behaviour modelling to ascertain the patient's readiness to uptake any educational interventions and behavioural attitudes towards self-management/improvement. The PULSE framework involves the calculation of the patient's CVD risk using the Systematic COronary Risk Evaluation (SCORE) algorithm, the estimation of readiness to change using the Transtheoretical Model (TTM) of intentional behaviour change, and the representation of personalization logic as Medical Logic Modules (MLM). The educational interventions were derived from evidencebased staged lifestyle modification materials and Canadian clinical guidelines for CVD risk management.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".