Bibliographic record
Abstract
Over the last three decades, an epidemic of obesity has markedly affected patients suffering from mental illnesses such as schizophrenia.Antipsychotic medications used to treat schizophrenia are considered as major culprits.The aim of this review is to first consider risk factors, to then outline negative sequelae of obesity for this population, and finally to address timing and content of recommended clinical interventions.Medical databases were searched with the terms ""weight," "obesity," and "schizophrenia."Selection of articles was guided by date of publication; recent papers are preferentially cited.The main findings were that, in addition to antipsychotic medications, socio-economics, lifestyle, immune factors, and circadian rhythms also contribute to obesity risk.A barrier to effective health promotion within psychiatry has been the concern that fears about gaining weight might stop individuals with schizophrenia from taking needed antipsychotic medication.Recommendations, therefore, are to keep the dose of antipsychotic medication as low as possible, avoid polypharmacy, encourage healthy eating and physical activity, address sleep problems and substance use, monitor weight, blood pressure, and metabolic parameters regularly, utilize motivational interviewing techniques and peer support, pay special attention to special needs such as those of women during pregnancy, and include bariatric surgery as a potential intervention.Conclusion: Besides careful attention to medication regimens, the literature supports the active encouragement and support of patient self-management strategies to both prevent and manage obesity in schizophrenia.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".