Effect of Obesity on Hypertension in Elderly
Bibliographic record
Abstract
Background: Obesity contributes to numerous and varied comorbid disease. Obesity is one of a constellation of markers for coronary heart disease and type 2 diabetes. This meta-analysis study aimed to assess the effect of obesity on hypertension in elderly. Subjects and Method: Meta-analysis and systematic review were conducted by collecting articles from Google Scholar, PubMed, Springer Link, and Science Direct databases. Keywords used “obesity” AND “hypertension” OR “high blood pressure” AND “elderly” OR “older people” AND “cross sectional”. The inclusion criteria were full text, using English or Indonesian language, using cross-sectional study design, and reporting adjusted odds ratio. The data were analyzed using Revman 5.3 program. Results: 6 studies from Netherland, Ethiopia, Singapura, Cina, Jerman, and Canada were selected for this study. Current meta-analysis study showed that obesity increased the risk of hypertension in elderly (aOR = 3.01; 95% CI= 2.44 to 3.72; p<0.01) with I2 = 61%. Conclusion: obesity increased the risk of hypertension in elderly. Keywords: obesity, hypertension, elderly Correspondence: Maria Imakulata Berek. Masters Program in Public Health, Universitas Sebelas Maret. Jl. Ir. Sutami 36A, Surakarta 57126, Central Java. Email: imma123433@gmail.com. Mobile: 085311622368.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.015 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".