Gender differences in metabolic syndrome risk factors among patients with serious mental illness
Bibliographic record
Abstract
The prevalence of metabolic syndrome and its components continue to increase among patients with serious mental illness. This cross-sectional study investigated whether metabolic syndrome prevalence and risk factors differ between male and female patients with serious mental illness. In total, 260 eligible patients were recruited from two hospitals. The data on demographic characteristics, lifestyle behaviour factors, biochemistry, and anthropometry were collected. Analyses were performed using multivariate logistic regression. Metabolic syndrome prevalence was 40.8% (35.1% in men and 46.8% in women). Among patients aged 40-49 years, metabolic syndrome prevalence was higher in men; however, the trend was reversed among patients aged 50 years or older. Notably, gender-specific metabolic syndrome risk factors were observed. In men, they included low education level, high body mass index (BMI), prolonged illness, comorbid physical illness, and diagnosis of bipolar disorder, whereas they included being married, old age, and high BMI in women. Our findings suggest that mental health professionals should consider the gender- and age-based metabolic syndrome prevalence trend in patients with serious mental illness when designing interventions for the study population to minimize metabolic syndrome prevalence.
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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 | 0.000 | 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) | 0.002 | 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".