Epidemiology of Hypertriglyceridemia in the Elderly Taiwanese population
Bibliographic record
Abstract
Our study used data collected in Chung-Hsing Village in Taiwan in May 1998 to evaluate the distribution of triglycerides and the association between hypertriglyceridemia and its correlates in elderly people. All individuals aged 65 and over were recruited as study subjects. A total of 1093 persons, out of 1774 registered residents, were contacted in face-to-face interviews. The response rate was 61.6%. However, only 586 respondents had blood tests and completed questionnaires. Analysis in this study was based on these 586 subjects. To study the significant correlates of hypertriglyceridemia, t-tests, ANOVAs, chi-square analysis and multivariate logistic regression were used. Among the study population, 66.0% were men and 34.0% were women. The mean age was 73.1 ± 5.3 years. The mean triglyceride values were 1.65 ± 0.93 mmol/L in men and 2.02 ± 1.44 mmol/L in women (p < 0.01). The proportions of hypertriglyceridemia were 18.7% in men and 27.6% in women (p < 0.05). After controlling the other covariates, analysis by multivariate logistic regression showed that the factors significantly related to hypertriglyceridemia were high systolic pressure, abnormal glutamic pyruvic transaminase, hypercholesterolemia and hyperglycemia. Thus, these results support the hypothesis that it is important to examine the other cardiovascular risk factors if one cardiovascular risk factor is observed. The data also suggest one should determine the triglyceride level when abnormal glutamic pyruvic transaminase is identified in an elderly subject.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".