[Association between waist circumference and the prevalence/control of hypertension by gender and different body mass index classification in an urban elderly population].
Bibliographic record
Abstract
OBJECTIVE: The aim of the present study was to evaluate the association between waist circumference and the prevalence/control of hypertension in an urban elderly population. METHODS: From September 2009 to June 2010, a population-based cross-sectional study was conducted in Wanshoulu area of Beijing, China. RESULTS: A total of 2 035 elderly (828 male, 1 207 females) participants aged ≥60 years from a community were included in this study for data analysis. We found that the increased waist circumference could significantly increase the risk of prevalence and poor control of hypertension, with the adjusted odds ratios (95% CI) as 1.04 (1.01-1.08) and 0.96 (0.92-1.00) , respectively. Among those identified pure central obesity females (64.7%) , the prevalence of hypertension was significantly higher than those females with normal body mass index (BMI) or with normal waist circumference (52.2%). The adjusted odds ratio (95%CI) between the above said groups appeared as 1.58 (1.07-2.32). The control rate of hypertension among females (32.9%) with pure central obesity, was lower than that of the females with normal BMI and waist circumference (43.5%) , with an adjusted odds ratio (95%CI) as 0.62 (0.37-1.04, P=0.071). CONCLUSION: There appeared significant association between people with pure central obesity and the increased risk of prevalence or with poor control of hypertension. More attention should be paid to both the prevalence and control of hypertension programs among females with pure central obesity.
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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 | 0.001 |
| 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) | 0.001 | 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 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".