Correlates of Metabolic Abnormalities in Bipolar I Disorder at Initiation of Acute Phase Treatment
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: Treatment of bipolar patients is often complicated by metabolic abnormalities such as obesity, diabetes, and dyslipidemia. We therefore evaluated the prevalence of these abnormalities and their correlates, in bipolar I patients, at the time of commencement of pharmacological treatment for acute mood episodes. METHODS: The study cohort consisted of 184 bipolar I patients hospitalized for treatment of acute mood episodes. Socio-demographic and clinical variables were noted and metabolic parameters, including body mass index, fasting plasma glucose, fasting total cholesterol, and current treatment(s) for diabetes and/or dyslipidemia were measured before initiating medication(s). RESULTS: Fifty-six (30.4%) subjects met our criteria for obesity; 80 (43.5%) had hyperglycemia, with 8 (4.3%) receiving anti-diabetic medication; and 38 (20.7%) had hypercholesterolemia, with 2 (1.1%) receiving cholesterol-lowering agents. We found that male sex (chi(2)=5.359, p=0.021), depressed or mixed state versus manic state (chi(2)=4.302, p=0.038), and duration of illness (t=2.756, p=0.006) were significantly associated with obesity. Older age (t=3.668, p<0.001), later age of disease onset (t=2.271, p=0.024), and lower level of educational attainment (beta=-0.531, p=0.001) were associated with hyperglycemia. CONCLUSION: Our finding that metabolic abnormalities are prevalent when initiating acute pharmacological treatment in bipolar I patients indicates that these factors should be integrated into treatment plans at the onset of disease management.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it