Defining Disparities in Cardiovascular Disease for American Indians
Bibliographic record
Abstract
BACKGROUND: Disparities in stroke and heart disease have been well defined in many populations in the United States. Relatively few studies, however, have assessed current disparities in cardiovascular disease in American Indian populations and compared trends with other regions of the United States. METHODS AND RESULTS: Using mortality data, age-adjusted all-cause, heart disease, and stroke mortality rates (per 100,000) were calculated for American Indians and whites from 1991 to 1995 and 1996 to 2000. The all-cause mortality rate was strikingly higher for American Indians than for whites. For example, during 1996 to 2000, the all-cause mortality rate for American Indians (1317, +/-61) was more than half again greater than that for whites (831, +/-8). Heart disease mortality declined significantly in whites (237 to 216 per 100,000) in Montana over the past decade and declined, although not significantly, in American Indians (326 to 283 per 100,000). Stroke mortality also declined significantly in whites (64 to 60 per 100,000) but not in American Indians (80 to 81 per 100,000) during this time period. The proportion of deaths before age 65 years for heart disease and stroke was considerably higher in Indian men (45% and 36%) and Indian women (29% and 28%) compared with white men (21% and 11%) and white women (8% and 7%). CONCLUSIONS: The disparity in heart disease and stroke mortality exists between American Indians and whites in Montana. Regional or state-level surveillance data will be needed to describe the changing patterns of heart disease and stroke mortality and cardiovascular risk factors in many native communities in the United States and Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".