Association of Ethnicity with Antipsychotic Dosage Using STRUCTURE Analysis
Bibliographic record
Abstract
Several studies have examined whether ethnicity as an independent factor can influence the individual's dosage of antipsychotics. However, there has been inconsistency in the results of these studies, particularly between white and non-white populations. This retrospective study tests the hypothesis of different dosing of antipsychotics in white Europeans vs. non-white Europeans considering both the self-reported ethnicity and the geographical ancestry calculated using 196 DNA markers.We collected self-reported ethnicity and DNA samples from 209 schizophrenia patients. We tested the association between self-reported and genetically-determined ethnicity with the chlorpromazine equivalent dose of each antipsychotic prescribed at the time of the assessment.We did not find any significant difference between self-reported white European -ethnicity and chlorpromazine equivalent doses (p=0.972). Furthermore, when we considered the geographical ancestry determined by the 196 SNPs, we could not find any correlation between the European ancestry and chlorpromazine equivalent dose.Our preliminary analysis shows that there is no evidence that different ethnic groups receive different dose of antipsychotics.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".