Overview: Towards individualized treatment in schizophrenia
Bibliographic record
Abstract
Abstract Schizophrenia is a serious mental disorder involving distortions of thinking or perception, inappropriate or blunted affect, and cognitive deficits may evolve in the course of time. Antipsychotics are the first choice for treatment; however, interindividual variability in response and side effects are commonly observed. To avoid time‐consuming, cost‐intensive, and potentially hazardous drug treatments, clinicians should ideally anticipate which antipsychotic is the most effective and less harmful for a given patient. This form of “individualised treatment” can only succeed if specific characteristics are identified as highly associated with the favourable response. Demographical, clinical, or physiological characteristics by themselves have not been shown to predict antipsychotic drug response to a clinically meaningful extent. As genetic factors are likely to contribute substantially to the efficacy and toxicity of drugs, numerous pharmacogenetic studies have searched for associations between gene variants and antipsychotic drug response. The first generation of pharmacogenetic studies yielded mainly negative and often inconsistent findings that are most likely the result of substantial heterogeneity among studies generally using small samples. Perhaps the most robust associations were found between polymorphisms of the serotonin 2A or the dopamine 2 receptor genes with response to clozapine or conventional antipsychotics, respectively. However, effect sizes are rather small and, therefore, further research is needed that integrates recent advances in genomics, proteomics, and biostatistics. Nonetheless, these findings are consistent with the dopamine/serotonin hypothesis in schizophrenia. The continuous discovery of new gene variants and progressive methodological improvements will help elucidate the molecular pathological mechanisms in schizophrenia, and reveal new avenues for drug development research. Drug Dev. Res. 60:75–94, 2003. © 2003 Wiley‐Liss, Inc.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".