M114. 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 pharmacological treatment. According to the American Psychological Association (APA) guidelines, treatment resistant status is defined as little or no symptom reduction to at least 2 antipsychotics at a therapeutic dose range for a trial of at least 6 weeks. The aim of the current study is to determine whether ethnicity and migration are associated with the development of treatment resistance in schizophrenia. Methods: In a sample of 251 participants with schizophrenia spectrum disorders, we conducted cross-sectional assessments to collect information regarding self-identified ethnicity, immigration history, and treatment history. Ancestry was identified using 292 genotype 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 ethnicity and treatment resistant schizophrenia regardless of whether European ethnicity was determined by self-report or genetic analysis. However, European Caucasians who were not born in Canada had lower likelihood of being treatment resistant. Conclusion: Due to the public healthcare system in Ontario, Canadians and residents of Canada of all ethnicities have equal access to treatment for their schizophrenia. This may explain why neither ethnicity nor migrant status was significantly associated with treatment resistance.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".