Hyperprolactinemia and estimated dopamine D2 receptor occupancy in patients with schizophrenia: Analysis of the CATIE data
Bibliographic record
Abstract
BACKGROUND: Large-scale data are still lacking on the relationship between serum prolactin concentration and dopamine D2 receptor occupancy in patients with schizophrenia treated with antipsychotics. METHODS: The dataset from 481 subjects (risperidone, N = 172, olanzapine, N = 211, and ziprasidone, N = 98) who participated in Phase 1 of the Clinical Antipsychotic Trials in Intervention Effectiveness (CATIE) was used in the present analysis. Dopamine D2 receptor occupancy levels on the day of the measurement of serum prolactin level were estimated from plasma antipsychotic concentrations. A multivariate general linear model was used to examine effects of clinical and demographic characteristics, including estimated D2 occupancy levels, on serum prolactin concentrations. Individual subjects were divided into two groups, stratified by the presence of hyperprolactinemia. To evaluate the performance of this binary classification, sensitivity, specificity, and accuracy of consecutive cut-off points in the D2 occupancy were calculated. RESULTS: The multivariate general linear model revealed that estimated D2 occupancy levels had significant effects on serum prolactin concentrations while any other variables failed to show significant effects. The cut-off point associated with 0.5 or greater, in both sensitivity and specificity with the greatest accuracy, was 73% (sensitivity, 0.58; specificity, 0.68; accuracy = 0.64) (68-70% for risperidone, 77% for olanzapine, and 55% for ziprasidone.). CONCLUSION: The threshold for hyperprolactinemia in D2 occupancy may lie somewhat on a lower side of the established therapeutic window with antipsychotics (i.e. 65-80%). This finding highlights the need for the use of the lowest possible dose to avoid this hormonal side effect in the treatment of schizophrenia.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".