Waist Circumference is a Sensitive Screening Tool for Assessment of Metabolic Syndrome Risk in Children Treated with Second-Generation Antipsychotics
Bibliographic record
Abstract
OBJECTIVE: To compare the prevalence of metabolic syndrome (MetS) and its components in second-generation antipsychotic (SGA)-treated and SGA-naive children; and to explore the utility of clinical markers, such as waist circumference (WC) and body mass index (BMI), as screening tools for MetS. METHODS: Subjects were prospectively recruited from the Psychiatry Emergency Unit at British Columbia Children's Hospital. As part of a quality-assurance project, a metabolic monitoring protocol was implemented, including collection of anthropomorphic and laboratory data. RESULTS: From January 2008 to February 2010, there were 117 SGA-treated and 217 SGA-naive children recruited. The overall prevalence of MetS was 19.0% (16/84; median treatment duration = 14 months) in SGA-treated and 0.8% (1/127) in SGA-naive children (OR 29.7; 95% CI 3.85 to 228.40, P < 0.001), with an increased prevalence of all components except high-density lipoprotein cholesterol (HDL-C), respectively: elevated WC (40.7% and 10.1%; P < 0.001); hypertriglyceridemia (33.7% and 18.8%; P = 0.01); impaired fasting glucose (12.5% and 0.7%; P = 0.005); and elevated blood pressure (41.2% and 16.5%; P < 0.001). SGA treatment was the strongest predictor of MetS (OR 19.2; 95% CI 2.30 to 160.44, P = 0.006) followed by male sex (OR 5.7; 95% CI 1.08 to 30.62, P = 0.04). Presence of abdominal obesity was more sensitive (92.9%) than BMI (68.8%), while fasting glucose of 5.6 mmol/L or more and HDL-C of 1.03 mmol/L or less were most specific (94.1%) in correctly identifying MetS. CONCLUSIONS: SGA treatment confers a significantly increased risk for MetS over the long term. WC measurement is a simple and sensitive screening tool for determining MetS risk in SGA-treated children. These data highlight the dangers of SGA treatment and the importance of standardized metabolic monitoring using sex- and age-adjusted tables in this population.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".