Original Research Risk of Weight Gain Associated with Antipsychotic Treatment: Results From the Canadian National Outcomes Measurement Study in Schizophrenia
Bibliographic record
Abstract
Background: Antipsychotic-induced weight gain occurs in a substantial percentage of treated persons. There re-mains a paucity of naturalistic data that describe relative weight-gain liability with the available novel atypical antipsychotics (NAPs). This investigation describes comparative NAP-induced weight gain in a prospective naturalistic cohort of persons with schizophrenia and related psychotic disorders. Methods: The Canadian National Outcomes Measurement Study in Schizophrenia (CNOMSS) is an ongoing prospective, longitudinal, naturalistic study involving 32 academic and community sites across Canada. Persons with DSM-IV–defined schizophrenia, schizophreniform or schizoaffective disorder, and psychosis not otherwise specified were consecutively enrolled. The overarching objectives of this initiative were to collect and compare global effectiveness, tolerability, safety, and humanistic outcomes in persons receiving commercially available NAPs in Canada. This analysis reports only weight change with the respective NAPs. Other outcomes were re-ported in separate companion papers. Results: A spectrum of weight-gain liability was noted with quetiapine (QUE) (mean 7.55 kg, SD 9.20; P = 0.28), olanzapine (OLZ) (mean 3.72 kg, SD 0.56; P = 0.15), and risperidone (RIS) (mean 1.62 kg, SD 7.72; P = 0.43). Categorically defined weight gain (that is, over 7 % of baseline weight) was observed in 55.6 % of QUE pa-tients, 24.1 % of OLZ patients, and 23.7 % of RIS patients. Adjusting for demographic and disease-specific con-
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".