Residual Lifetime Risk of Cardiovascular Diseases in Japan
Bibliographic record
Abstract
Risk assessment of cardiovascular diseases (CVD) is shifting from the relative risk to an absolute risk approach. The residual lifetime risk (LTR), which provides an absolute risk assessment, is an epidemiologic measure that expresses the probability of someone of a given age and sex developing a disease condition during their remaining lifespan. The LTR estimation is important because it could be more easily comprehended by clinicians and patients. The LTR for CVD was not estimated for the Japanese population until recently, when the LTR of stroke and acute myocardial infarction (AMI) was reported. The reported LTR of stroke and AMI for middle-aged adults is substantial. The observed probabilities illustrated that approximately 1 in 5 men and women of middle age will suffer from a stroke in their remaining lifetime. On the other hand, approximately 1 in 6 men and 1 in 9 women of middle age will suffer from AMI in their remaining lifetime. Aaginst the backdrop of the aging population and worsening risk factor scenario, these estimates re-emphasize that CVD is a public health burden that requires preventive interventions. These estimates provide a means to communicate the absolute risk of CVD to the lay population, policy makers, as well as health service providers in predicting the disease burden of CVD. This easily comprehended knowledge can be used as an important index to assist in public health education and planning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".