Social Determinants of Health and Preclinical Glycemic Control in Newly Diagnosed First-Episode Psychosis Patients
Bibliographic record
Abstract
BACKGROUND: The abnormally high incidence of disorders of glucose metabolism (DGM) in psychotic-spectrum disorders (PSD) has often been attributed to the side effects of antipsychotics and unhealthy lifestyles. The influence of social determinants of health has been largely ignored, despite ample evidence linking social adversity with both PSD and DGM. The aim of this study is to examine the influence of well-established social determinants of health on preclinical levels of glycated hemoglobin (HbA1c) in a sample of first-episode psychosis (FEP) patients. METHODS: In a sample of newly admitted FEP patients, univariate analyses were used to select the main predictors of HbA1c levels from the following social determinants of health: childhood trauma, immigrant background, visible minority status, and indices of social and material deprivation. The predictors identified in the univariate analyses were tested in multivariate linear regression models including age, sex, BMI, depression, and physical anergia (proxy of sedentary behaviour) as covariates. RESULTS: Univariate analyses identified visible minority status and childhood physical abuse as predictors of HbA1c. After controlling for covariates, minority status significantly predicted higher levels of glycated hemoglobin (β = 0.23; P = 0.01), and physical abuse had a marginally significant effect (β = 0.23; P = 0.06). Other predictors were not significantly associated. CONCLUSION: FEP patients from a visible minority or who were victims of childhood physical abuse have higher levels of HbA1c at admission compared with other patients. This might suggest an increase in risk for the development of future DGM. If confirmed, preventive strategies could be tailored for these groups.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".