Model to assess the factors of 10-year future risk of coronary heart disease among people of Framingham, Massachusetts
Bibliographic record
Abstract
In earlier decade, heart disease was the most common cause of death in the US. Among the many important risk factors such as age, number of cigarettes smoke could help in determining the odds of having corona heart disease (CHD) when modeling with other important factors. We analyzed ongoing cardiovascular study on residents of the town of Framingham, Massachusetts, US to predict the 10-year risk of future CHD. We applied the Binary logistic regression model to assess the strength of the association of factors (such as gender, age, number of cigarettes smoke, total cholesterol level) in predicting the odds of having CHD in study population. Results showed that gender, age, number of cigarette smoke, systolic blood pressure were statistically significant and the increased age and cigarettes per day increase the odds of having 10-year risk of CHD. However, the noticeable finding was that patients with Diabetes at higher glucose level have the higher odds of having 10-year risk of CHD than with low level of glucose concentration among the residents of Framingham study.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".