Early Stabilization of Weight Changes Following Treatment With Olanzapine, Risperidone, and Aripiprazole
Bibliographic record
Abstract
OBJECTIVE: The study objective was to examine whether and when antipsychotic-induced weight gain in first episode psychosis (FEP) stabilizes over a 12-month exposure to the same antipsychotic in a sample of previously untreated FEP patients. METHODS: In this prospective naturalistic outcome study, 109 patients diagnosed with non-affective or affective psychosis (DSM-IV) were treated with the same antipsychotic medication (olanzapine n = 45, risperidone n = 39, or aripiprazole n = 25) throughout the first year of treatment. Body weight was measured and body mass index calculated at baseline and 1, 2, 3, 6, 9, and 12 months. Additional weight data over the second year were available, making extending the comparison for a second year possible. RESULTS: Linear mixed model analysis showed a significant main effect of time (Type III test P < .001) after adjusting for baseline weight values. Post hoc pairwise comparisons showed that incremental weight changes subsequent to month 6 were insignificant, suggesting weight stabilization by month 9. No significant difference (P = .243) between groups or time × group interaction (P = .111) was observed. Similar findings were obtained with BMI. A follow-up analysis, of a subsample who continued treatment with the same antipsychotic for an additional 12 months (n = 57), confirmed weight stabilization in the second year. There was no significant main effect of time (P = .641), group (P = .539), or time × group interaction (P = .250). CONCLUSIONS: Antipsychotic-induced weight gain occurs mostly in the first few months of treatment. Preventive interventions concurrent to second-generation antipsychotic treatment initiation in medication-naive FEP patients might be warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".