An exploratory decision tree analysis to predict cardiovascular disease risk in African American women.
Bibliographic record
Abstract
OBJECTIVE: African American (AA) women are at greater risk for cardiovascular disease (CVD) compared to White women, which can be attributed to disparities in risk factors. The built environment may contribute to improving CVD risk factors by increasing physical activity (PA). This study used recursive partitioning, a multivariate decision tree risk classification approach, to determine which built environment characteristics contributed to the classification of AA women as having 4 or more CVD risk factors at optimal levels. METHOD: Recursive partitioning has the ability to detect interactions and does not have sample size limitations to detect effects. The Classification and Regression Trees (CR&T) growing method was used to group participants as having 4 or more versus 3 or fewer risk factors at optimal levels. Risk factors were smoking, body mass index (BMI), PA, healthy diet, cholesterol, glucose, and blood pressure. Built environment predictors were presence and quality of neighborhood PA resources (PARs), walkability, traffic safety, and crime. RESULTS: Participants (N = 30, mean age of 54.1 ± 7.5) all had at least 1 risk factor at the optimal level, none had all 7, and 66.7% had 4 or more risk factors at optimal levels. The CR&T identified participants with few, low-quality neighborhood PARs and who were older than 55 as least likely to have 4 or more CVD risk factors at optimal levels. CONCLUSION: Being younger than 55 years old and having many, high-quality neighborhood PARs may predict lower risk for CVD in AA women. Results should be used in future studies with larger sample sizes to inform logistic regression models. (PsycINFO Database Record
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".