Development and validation of a cardiovascular disease risk-prediction model using population health surveys and dietary indices: the Cardiovascular Disease Population Risk Tool – Nutrition (CVDPoRT-Nutrition).
Bibliographic record
Abstract
ObjectivesOnly a few cardiovascular risk prediction models have been developed based on modifiable exposures and even fewer utilize complex dietary factors. Our objective was to develop and validate the Cardiovascular Disease Population Risk Tool (CVDPoRT)-Nutrition as a tool for estimating 5-year risk of incident cardiovascular disease (CVD) using lifestyle factors and dietary pattern scores. ApproachThe CVDPoRT developed and validated using the Canadian Community Health Survey (CCHS) linked with health administrative databases was modified to remove limited measures of dietary intakes (i.e., fruit and vegetable, potato, and juice intake frequency) and instead incorporate 5 different dietary pattern scores (i.e., Dietary Guidelines for Americans Adherence Index, Dietary Approaches to Stop Hypertension, Healthy Eating Index, Alternative HEI, and Mediterranean Style Dietary Pattern Score). Outcome data (i.e., CVD events and CVD-related mortality) came from linkage with the Canadian Vital Statistics – Death Database and Discharge Abstract Database. CVDPoRT-Nutrition was tested in 61 policy-relevant subgroups. ResultsPerformance after adding in each dietary pattern score was similar to the original CVDPoRT (Brier score=2.6%, Harrell’s c-stat=0.87(0.85-0.88) for female models; Brier score=1.6%, Harrell’s c-stat=0.82(0.81-0.84) for male models). The algorithm was calibrated in 53 (female models) and 57 (male models) of 61 policy relevant subgroups. The most important predictors of CVD and CVD-related mortality were age, sex, and smoking. ConclusionAltering the dietary measures included in the CVDPoRT algorithm did not greatly improve the predictive capacity. The original CVDPoRT can continue to be used for predicting CVD and CVD-related mortality, while CVDPoRT-Nutrition may be used for predicting CVD incidence associated with poor dietary patterns.
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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 | 0.028 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".