Second Generation Antipsychotics (SGAs) in Schizophrenic Patients and Bipolar Disorder: Correlation With Metabolic Syndrome (NCEP ATP III(a))
Bibliographic record
Abstract
INTRODUCTION: The Metabolic Syndrome is a set of diverse clinical situations such as diabetes mellitus, hypertension and dyslipidemia. Patients with mental illnesses such as schizophrenia or bipolar disorder have a higher mortality than the general population attributable in 60% to somatic diseases and metabolic syndrome, where second generation antipsychotics increase the risk of weight gain and insulin resistance. Objectives. Correlate the treatment with second generation antipsychotics (SGAs) as a possible predictor for Metabolic Syndrome according to the NCEP ATP III (a) classification. METHODS: Descriptive, cross-sectional correlational study. The sample was of 92 patients, applying an open and convenience sampling due to the mental state of the patients in order to determine their degree of acceptance to the study (Informed Assent) and consent to the legal guardian as the main inclusion criterion. For the analysis, the following variables were considered: blood pressure, weight, height, abdominal circumference, serum levels of triglycerides, glucose and high density lipoproteins. The SPSS 20.0 ® program was used logistic regression analysis with a p-value <0.05 and a confidence level of 95%. RESULTS: SGAs most used was clozapine (54.3%). The correlation analysis showed that sociodemographic aspects such as personal history, habits, physical activity and paraclinical and anthropometric records correlated with the possible diagnosis of metabolic syndrome (p <0.05), but not with SGAs (p> 0.05). ). CONCLUSION: No correlation was found between the presence of the metabolic syndrome and the type of antipsychotic treatment.
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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 | 0.002 |
| 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) | 0.002 | 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".