Prevalence of metabolic syndrome, its continuous severity score, and correlated cardiovascular risk among postmenopausal women of a selected rural area of Bangladesh
Bibliographic record
Abstract
Background: Our primary objective was to estimate the prevalence of metabolic syndrome (MetS) among postmenopausal women (PMW) and evaluate the correlation of its severity score with the risk of cardiovascular diseases (CVD). In addition, we compared the distribution of CVD risk and risk factors among PMW with or without MetS. Methods: We recruited 265 PMW of 40-70 years of age from February to December 2016 who had no CVD. The MetS was defined according to modified Adult Treatment Panel III criteria and MetS severity score was constructed using a standardized Z score. CVD risk was assessed using the lab-based Globorisk score. Results: About 35.1% of the PMW had MetS. The proportion of central obesity, generalized obesity, physical inactivity, diabetes, and hypertension were higher among those with MetS than those without. A highly significant CVD risk score difference (P<0.001) was observed between the subjects with or without MetS. Similarly, CVD risk showed a significant linear correlation (P<0.001) with the MetS severity score, which was adjusted for several confounders. Conclusion: One-third of PMW in a selected rural area of Bangladesh had MetS and its severity score showed a significant correlation with CVD risk. A large-scale study is warranted to confirm the current findings with more precision. Bangabandhu Sheikh Mujib Medical University Journal 2023;16(3): 133-138
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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) | 0.001 | 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".