Role of Ethnicity in Antipsychotic-Induced Weight Gain and Tardive Dyskinesia: Genes or Environment?
Why this work is in the frame
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Bibliographic record
Abstract
AIM: This study explored the role of self-reported ethnicity and genetic ancestry on antipsychotic (AP)-induced weight gain and tardive dyskinesia (TD) in schizophrenia. PATIENTS & METHODS: Ethnicity was determined by self-report as well as Structure analysis of 190 SNPs selected from HapMap3, genotyped using a customized Illumina BeadChip. Age, gender, baseline weight and AP response using Brief Psychiatric Rating Scale were assessed. Multivariate regression models for AP-induced weight gain and TD, based on the Abnormal Involuntary Movement Scale were constructed. RESULTS: African-American ethnicity (self-report, p = 0.021 and Structure analysis, p = 0.042) predicted AP-induced weight gain but not TD (self-report, p = 0.408 and Structure analysis, p = 0.714). CONCLUSION: Self-reported African-American ethnicity seemed to better predict AP-induced weight gain in schizophrenia compared with genetic ancestry, suggesting a possible role of environmental in addition to genetic factors. Future larger studies are needed to clarify specific gene-environment mechanisms mediating the effect of ethnicity on AP-induced weight gain.
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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) | 0.001 | 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 it