P-1302 - Relationship between metabolic syndrome and clinical features of schizophrenia
Bibliographic record
Abstract
The aim of this study was to evaluate the prevalence of metabolic syndrome and metabolic syndrome criteria in patients with schizophrenia and also to investigate the effects of metabolic syndrome on medical treatment, clinical course. One hundred-sixteen patients with schizophrenia were consecutively admitted. Waist circumference, blood pressure, body weight and height were measured, and body mass index was calculated. Brief Psychiatric Rating Scale, Scale for the Assessment of Positive Symptoms, Scale for the Assessment of Negative Symptoms, Calgary Depression Scale for Schizophrenia were applied to the patients. The frequency of metabolic syndrome according to IDF criteria was 42.2% among the patients (46.9% for female and 38.8% for male patients). There was no significant difference between patients with and without metabolic syndrome in terms of age. The frequencies of metabolic syndrome were 62.5% for together taken typical and atypical antipsychotics and 35.7% for taken two or more atypical antipsychotics, whereas the rates of metabolic syndrome of taken only one atypical and only one typical antipsychotics were 45.1% and 13.3%, respectively. The duration of disease in patients with metabolic syndrome was higher than those without. In this study, the frequency of metabolic syndrome in patients with schizophrenia was consistent with the results of previous studies in our country. Our findings showed that the duration of illness, high scores of BMI, use of clozapine or concurrent use of typical and atypical antipsychotics, depressive and negative symptoms of schizophrenia were significant risk factors.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".