Sex Differences in Obesity and Cognitive Function in Chinese Elderly Patients With Chronic Schizophrenia
Bibliographic record
Abstract
Background: It is well known that schizophrenia is associated with sex differences. However, no study has explored the sex differences in obesity and cognitive function in elderly Chinese patients with schizophrenia. Objective: This study aimed to compare sex differences in obesity and cognitive function in elderly Chinese individuals with schizophrenia. Methods: A total of 304 elderly patients with schizophrenia and 130 sex- and age-matched healthy controls from the community were recruited. Demographic, clinical, and lipid parameters were collected for all subjects. The Montreal Cognitive Assessment (MoCA) was used to assess the global cognitive functions of the participants, while the Positive and Negative Syndrome Scale (PANSS) was used to assess psychopathological symptoms in patients with schizophrenia. Results: Of the patients with schizophrenia, the prevalence of obesity in men and women was 11.7% (19/163) and 21.3% (30/141), respectively. The score (14.51 ± 6.504) of MOCA in elderly male patients with schizophrenia was significantly higher than that (11.40 ± 6.822) in female patients. There was a positive correlation between the MOCA scores and body mass index (BMI) (r=0.206, p=0.018) in male elderly patients with schizophrenia. Conversely, the MOCA scores of female elderly patients with schizophrenia did not correlate with BMI (p>0.05). However, we found no sex differences in obesity and cognition among control older adults. Conclusions: Our findings suggest that there are significant sex differences in obesity and cognitive function in elderly Chinese patients with schizophrenia.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".