Overview: Towards individualized treatment in schizophrenia
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
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.
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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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