A Randomized, Double-Blind, Placebo-Controlled Pilot Study of Naltrexone to Counteract Antipsychotic-Associated Weight Gain
Bibliographic record
Abstract
Patients with schizophrenia experience higher rates of obesity as well as related morbidity and mortality than the general population does. Women with schizophrenia are at particular risk for antipsychotic-associated weight gain, obesity, and related medical disorders such as diabetes and cardiovascular disease. Given preclinical studies revealing the role of the endogenous opioid systems in human appetite and the potential of antipsychotic medications to interfere with this system, we hypothesized that opioid antagonists may be beneficial in arresting antipsychotic-associated weight gain and promoting further weight loss in women with schizophrenia. In the present study, 24 overweight women with a diagnosis of schizophrenia or schizoaffective disorder were randomized to placebo or naltrexone (NTX) 25 mg/d for 8 weeks. The primary outcome measure was a change in body weight from baseline. The patients in the NTX group had significant weight loss (-3.40 kg) compared with weight gain (+1.37 kg) in the patients in the placebo group. Mainly, nondiabetic subjects lost weight in the NTX arm. These data support the need to further investigate the role of D2 blockade in reducing food reward-based overeating. A larger study addressing the weaknesses of this pilot study is currently underway.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".