Course and predictors of weight gain in people with first-episode psychosis treated with olanzapine or haloperidol
Bibliographic record
Abstract
BACKGROUND: Substantial weight gain is common with many atypical antipsychotics. AIMS: To evaluate the extent, time course and predictors of weight gain and its effect on study retention among people with first-episode psychosis treated with olanzapine or haloperidol. METHOD: Survival analysis assessed time to potentially clinically significant weight gain (> or =7%) and the effect of weight gain on study retention. Weight gain during the 2-year study was summarised using last-observation-carried-forward (LOCF), observed cases and study completion approaches. RESULTS: After 2 years of treatment, LOCF mean weight gain was 10.2 kg (s.d.=10.1) for olanzapine (n=131) and 4.0 kg (s.d.=7.3) for haloperidol (n=132); observed cases mean weight gain was 15.4 kg (s.d.=10.0) for olanzapine and 7.5 kg (s.d.=9.2) for haloperidol. Change in body mass index was significantly predicted only by treatment group (P < 0.0001). CONCLUSIONS: Olanzapine was associated with significantly greater weight gain than haloperidol, with both leading to greater weight gain than previously described.
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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 | 0.001 |
| 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 | 0.001 |
| 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".