Incidence of Hyper Lipidemia with Atypical Antipsychotic Treatment
Bibliographic record
Abstract
Background: Atypical Antipsychotics(AAPS) have been found to be effective in various psychiatric conditions however evidence of treatment emergent hyperlipidemia limit their clinical benefits. Objective: To determine the treatment effects of AAPS onTriglyceride. Method: Patients with schizophrenic and non-schizophrenic conditions were randomly started on AAPS. Weight, BMI, and triglycerides were measured every 3 months from base line for one year. 121 enrolled, 119 completed 1 year follow up. Olanzapine =51 Risperidone = 59 Quetiapine = 8 Clozapine = 1 Data analysis: Descriptive and inferential analysis was carried except Clozapine to examine effects on weight, BMI and triglycerides. Results: WEIGHT GAIN more than 7% Males : Risperidone 46% > Quetiapine 33% >Olanzapine 16% Females: Quetiapine 40% >Olanzapine 35% >Risperidone 9% BMI: OBESITY: Males: Quetiapine 33.33% >Olanzapine 32.26%> Risperidone 16.22% Females: Risperidone 50%> Quetiapine 20% >Olanzapine 20% MEAN TRIGLYCERIDES: Male: Risperidone 1.92> Olanzapine 1.7 Females: Olanzapine 1.97 > Risperidone 1.78 HYPERLIPIDEMIA: Male: Olanzapine 54.84% >Risperidone 43.24%>Quetiapine 33.33% Female: Quetiapine 80% > Olanzapine 65% > Risperidone 45.45% Higher incidence with Risperidone in 20 to 40 age group; with Olanzapine in 61 plus. Higher incidence in Schizophrenia obesity 42.42% and hyperlipidemia 37.10%. Conclusion: Results show that AAPS do affect weight, BMI and Triglycerides in varying degree to age, gender and disease, having significant clinical implications that warrant close monitoring with ongoing education on life style, diet and exercise in a heuristic manner.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".