Effect of atypical antipsychotics on body weight in geriatric psychiatric inpatients
Bibliographic record
Abstract
Background: Studies suggest that antipsychotic-induced weight gain is not a great concern in the elderly population. This study investigated the weight change in elderly patients with various treatment duration and antipsychotics. Part 1 of the study was to determine whether atypical antipsychotics induced weight change in elderly patients. Part 2 was to determine whether certain atypical antipsychotics induced more weight change in elderly patients. Methods: In Part 1, a retrospective chart review was done on 115 geriatric inpatients. After exclusion, patients were divided into four groups: control ( n = 17), new treatment ( n = 18), long-term treatment ( n = 13), and medication switch groups ( n = 8). In Part 2, a retrospective medication review was performed on 169 geriatric inpatients. After exclusion, patients were divided into three groups: aripiprazole ( n = 18), olanzapine ( n = 49), and risperidone ( n = 57). Body weights were obtained at two different time points. Results: No significant difference in weight change was observed among the control (1.5 kg), new treatment (0.8 kg), long-term treatment (−0.3 kg), and medication switch (1.9 kg) groups. No significant difference in weight change was observed between patients with and without dementia (0.8 and 1.1 kg, respectively). The weight change in the aripiprazole group (−2.0 kg; −2.30% from baseline) was significantly different from the weight change in the olanzapine group (0.7 kg; 1.87% from baseline; p < 0.05), but not from the risperidone group (−0.4 kg; −0.45% from baseline). Clinically significant weight gain (>7% increase in body weight) occurred in 14.3% of the olanzapine patients, a percentage significantly higher than the 3.5% in the risperidone group. Conclusion: Although atypical antipsychotics were generally weight neutral in the geriatric population, aripiprazole and olanzapine were associated with significant weight loss and weight gain, respectively.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".