Demographic, biochemical, clinical, and cognitive symptom differences between smokers and non-smokers in Chinese older male patients with chronic schizophrenia
Bibliographic record
Abstract
BACKGROUND: Several studies have suggested that smoking may impair cognitive function and worsen psychiatric symptoms in people with schizophrenia, but the results have not been consistent. There have been few studies to date that have examined the effects of smoking in older men with chronic schizophrenia. METHODS: The participants in our study consisted of 167 order Chinese males with chronic schizophrenia and 359 normal control subjects. We split them into smoking and non-smoking groups based on whether or not they smoked. Second, we compared their differences in terms of general demographic characteristics (such as age, education, body mass index, age of illness onset, and course of disease), disease information (such as hypertension, diabetes, and hyperlipidemia), lifestyle factors (such as physical exercise and lunch break), blood biochemical indicators (such as albumin, triglyceride, total cholesterol, high-density lipoprotein, low-density lipoprotein and fasting blood glucose), and medication usage (such as clozapine, olanzapine, risperidone, and chlorpromazine). Lastly, a neuropsychological test battery was used to assess their psychiatric and cognitive symptoms, for example, the Montreal Cognitive Assessment (MoCA) was used to assess their overall cognitive functioning. Their depressive symptoms were assessed by the geriatric depression scale (GDS). Activities of daily living (ADL) were used to assess their ability to lead a daily life, while the positive and negative syndrome scales (PANSS) were used to assess their psychiatric symptoms. RESULTS: Smokers who develop schizophrenia at older ages had a higher body mass index than non-smokers. We also found that plasma albumin, triglycerides, low-density lipoprotein, and fasting blood glucose concentrations were significantly higher in smokers. In contrast, smokers with schizophrenia also had lower PANSS total scores, negative symptom scores, and general psychopathology scores. A forward stepwise binary logistics regression analysis demonstrated a significant association between negative symptom scores and smoking status (B = 0.112, p < 0.001, OR = 1.119, 95% confidence interval: 1.059-1.181). Correlation analysis was carried out and it was found that the amount of cigarette consumption per day had a negative correlation with plasma albumin level(r = - 0.290, p = 0.004). However, no such association was found in normal controls. CONCLUSIONS: Elderly Chinese men with schizophrenia have a higher percentage of smokers, and although smoking can reduce their plasma albumin levels, it does contribute to the prevention of negative symptoms.
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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 | 0.001 | 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".