A translational research approach to poor treatment response in patients with schizophrenia: clozapine–antipsychotic polypharmacy
Bibliographic record
Abstract
Poor treatment response in patients with schizophrenia is an important clinical problem, and one possible strategy is concurrent treatment with more than one antipsychotic (polypharmacy). We analyzed the evidence base for this strategy using a translational research model focused on clozapine-antipsychotic polypharmacy (CAP). We considered 3 aspects of the existing knowledge base and translational research: the link between basic science and clinical studies of efficacy, the evidence for effectiveness in clinical research and the implications of research for the health care delivery system. Although a rationale for CAP can be developed from receptor pharmacology, there is little available preclinical research testing these concepts in animal models. Randomized clinical trials of CAP show minimal or no benefit for overall severity of symptoms. Most studies at the level of health services are limited to estimates of CAP prevalence and some suggestion of increased costs. Increasing use of antipsychotic polypharmacy in general may be a factor contributing to the under-utilization of clozapine and long delays in initiating clozapine monotherapy. Translational research models can be applied to clinical questions such as the value of CAP. Better linkage between the components of translational research may improve the appropriate use of medications such as clozapine in psychiatric practice.
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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 | 0.051 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".