The effect of ethnicity and immigration on treatment resistance in schizophrenia
Bibliographic record
Abstract
BACKGROUND: Treatment resistance is a common issue among schizophrenia patients undergoing antipsychotic treatment. According to the American Psychiatric Association (APA) guidelines, treatment-resistant status is defined as little or no symptom reduction to at least two antipsychotics at a therapeutic dose for a trial of at least six weeks. The aim of the current study is to determine whether ethnicity and migration are associated with treatment resistance. METHODS: In a sample of 251 participants with schizophrenia spectrum disorders, we conducted cross-sectional assessments to collect information regarding self-identified ethnicity, immigration and treatment history. Ancestry was identified using 292 markers overlapping with the HapMap project. Using a regression analysis, we tested whether a history of migration, ethnicity or genetic ancestry were predictive of treatment resistance. RESULTS: Our logistic regression model revealed no significant association between immigration (OR = 0.04; 95%CI = 0.35-3.07; p = 0.93) and treatment resistant schizophrenia. White Europeans did not show significant association with resistance status regardless of whether ethnicity was determined by self-report (OR = 1.89; 95%CI = 0.89-4.20; p = 0.105) or genetic analysis (OR = -0.73; 95%CI = -0.18-2.97; p = 0.667). CONCLUSION: Neither ethnicity nor migrant status was significantly associated with treatment resistance in this Canadian study. However, these conclusions are limited by the small sample size of our investigation.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".