Diabetes and Diabetes Care among Nonobese Japanese-Americans: Findings from a Population-Based Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study are as follows: (1) to determine the prevalence of diabetes among nonobese Japanese-Americans and to determine the adjusted odds of diabetes among nonobese Japanese-Americans compared to non-Hispanic Whites (NHWs); (2) to identify the risk factors associated with having diabetes in a large sample of nonobese Japanese-Americans; and (3) to determine the prevalence and adjusted odds of diabetes management behaviors among nonobese Japanese-Americans with diabetes in comparison to NHWs with diabetes. METHODS: The combined 2007-2016 waves of the adult California Health Interview Survey (CHIS) were used to analyze a nonobese (BMI<30) sample of 2,295 Japanese-Americans and 119,651 NHWs. Chi-square and logistic regression analyses were performed using Stata. RESULTS: The findings of this representative community study of nonobese Californians indicate that the prevalence of diabetes among Japanese-American respondents was higher than their NHW counterparts (8.0% versus 4.5%). Prevalence increased markedly with age; one-quarter of nonobese Japanese Americans aged 80 and older had diabetes. CONCLUSIONS: The prevalence of diabetes among nonobese Japanese-Americans is significantly higher than that among NHWs. There is an urgent need to develop appropriate intervention and prevention approaches with lifestyle modification specifically targeted towards nonobese Japanese-Americans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it