Psychiatric treatment: A risk factor for obesity?
Bibliographic record
Abstract
BACKGROUND: People with psychiatric diagnoses have increased physical health difficulties. OBJECTIVES: To examine the physical growth parameters documented in children receiving psychiatric treatment. METHODS: A chart review was performed on consecutive paediatric consultations in 1997 and 1998 on 34 children six to 12 years of age admitted to an intermediate-stay psychiatric inpatient service. Growth parameters of each child were plotted on standard growth curves. The prevalence of obesity (body mass index at or above the 95th percentile), absolute weight at or above the 95th and 50th percentiles, underweight status, tall and short stature, macrocephaly and microcephaly were calculated. The prevalence of atypical findings was compared with the expected prevalence of typical growth parameters in the general population. Risk factors for atypical growth parameters were recorded. An association between weight and specific medication use was explored. RESULTS: It was found that 11.8% of the children were obese. It was also found that 23.5% of the children had weight at or above the 95th percentile, 79.3% had weight at or above the 50th percentile, 14.7% had macrocephaly and 79.4% had a head circumference above the 50th percentile; these results were statistically significant. The mean number of psychotropic medications prescribed was 6.4, although there was no significant association between higher weight and current medication type. CONCLUSIONS: Children receiving inpatient psychiatric treatment were more likely to have higher weight than typical children. Monitoring growth parameters is an important component of the paediatric care of children with psychiatric diagnoses. Guidelines are required for obesity prevention and intervention in the context of the risk factors experienced by this high risk population.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".