Sex, Race, and Smoking Impact Olanzapine Exposure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Response to antipsychotics is highly variable, which may be due in part to differences in drug exposure. The goal of this study was to evaluate the magnitude and variability of concentration exposure of olanzapine. Patients with Alzheimer's disease (n = 117) and schizophrenia (n = 406) were treated with olanzapine as part of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE). Combined, these patients (n = 523) provided 1527 plasma samples for determination of olanzapine concentrations. Nonlinear mixed-effects modeling was used to determine the population pharmacokinetics of olanzapine, and patient-specific covariates were evaluated as potential contributors to variability in drug exposure. The population mean olanzapine clearance and volume of distribution were 16.1 L/h and 2150 L, respectively. Elimination of olanzapine varied nearly 10-fold (range, 6.66-67.96 L/h). Smoking status, sex, and race accounted for 26%, 12%, and 7% of the variability, respectively (P < .0001). Smokers cleared olanzapine 55% faster than non/past smokers (P < .0001). Men cleared olanzapine 38% faster than women (P < .0001). Patients who identified themselves as black or African American cleared olanzapine 26% faster than other races (P < .0001). Differences in olanzapine exposure due to sex, race, and smoking may account for some of the variability in response to olanzapine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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