Trajectories of daily antipsychotic use and weight gain in people hospitalized for the first episode of psychosis
Bibliographic record
Abstract
BACKGROUND: We need to better understand the risk factors and predictors of medication-related weight gain to improve metabolic health of individuals with schizophrenia. This study explores how trajectories of antipsychotic medication (AP) use impact body weight early in the course of schizophrenia. METHODS: = 92) during their first psychiatric hospitalization. We prospectively collected weight, body mass index (BMI), metabolic markers, and exact daily medication exposure during 6-week hospitalization. We quantified the trajectory of AP medication changes and AP polypharmacy using a novel approach based on meta-analytical ranking of medications and tested it as a predictor of weight gain together with traditional risk factors. RESULTS: = 29). Then, 48% of individuals remained on their first prescribed medication, while 33% of people remained on monotherapy. Almost half of the individuals (39/92) experienced escalation of medications, mostly switch to AP polypharmacy (90%). Only baseline BMI was a predictor of BMI change. Individuals in the top tercile of weight gain, compared to those in the bottom tercile, showed lower follow-up symptoms, a trend for longer prehospitalization antipsychotic treatment, and greater exposure to metabolically problematic medications. CONCLUSIONS: Early in the course of illness, during inpatient treatment, baseline BMI is the strongest and earliest predictor of weight gain on APs and is a better predictor than type of medication, polypharmacy, or medication switches. Baseline BMI predicted weight change over a period of weeks, when other traditional predictors demonstrated a much smaller effect.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".