Predictors of Weight Gain and Metabolic Indexes among Men Admitted to Forensic Psychiatric Hospital
Bibliographic record
Abstract
People with mental health disorders face elevated risk of metabolic syndrome (MetS), which increases the risk of serious health problems and premature mortality. Obesity is prevalent among those hospitalized in forensic psychiatric units, and substantial weight gains during hospitalization have been reported. We examined International Diabetes Federation (IDF) criteria and proxy MetS indexes (body mass index [BMI], blood pressure, and waist circumference) in the medical records of 527 men admitted to a forensic hospital, and tested predictors of weight gain during their first year or less in hospital. IDF indexes were documented for 22% of men whereas proxy indexes were documented for 46%. Both suggested similar MetS prevalence: 16% IDF, 17% proxy. Weight gain averaged 1.72 kg per month; BMI, being a smoker, and length of stay were independent predictors. Interventions focusing on these risk factors are advisable in order to support both mental and physical health among individuals admitted to forensic psychiatric services. The proxy MetS indexes offer a rapid screening measure and a promising tool for research studies and clinical practice in the absence of blood test results.
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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 | 0.002 |
| 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 | 0.000 | 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".