M199. COPY NUMBER VARIANCE (CNV) ANALYSIS TO DETERMINE OPTIMAL ANTIPSYCHOTIC DOSAGE IN SCHIZOPHRENIA: A PILOT STUDY
Bibliographic record
Abstract
Abstract Background The relationship between genetic polymorphisms of antipsychotic drug-metabolizing agents and drug response has been thoroughly investigated and analyzed. However, from a pharmacokinetic standpoint, few studies have explored the relationship between Copy number variants (CNV) and antipsychotic dosage. The aim of the present study is to test the association between antipsychotic dosage and CNV in schizophrenia (SCZ) patients. Methods The current dosage of antipsychotic medications was collected from 263 schizophrenia patients. The dosage was standardized using three different methods: chlorpromazine equivalent(CPZe), defined daily dose (DDD), and percentage of maximum dose (PM %). The patients were then genotyped using the Illumina HumanOmni2.5–8 BeadChip Kit. Results The CNV analysis did not show that CNVs are associated with dosage variation for CPZe, PM %, and DDD. Discussion In this pilot sample, we investigated for the first time CNVs and standardized antipsychotic dosage. The relationship between CNV and optimal antipsychotic dosage has far reaching clinical implications. Further analysis is required that utilitze large prescription databases to build on the results presented in this pilot study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".