Pharmacogenetics in Psychiatry: Precision medicine or ‘mystic, magic, and mysterious’?
Bibliographic record
Abstract
Prescribers and patients are frequently facing the challenge that treatment standards established at a population level might not be beneficial at the individual level. As a result, lengthy trials are often required before the optimum psychiatric medication treatment, single or in combination, is found. The underlying reasons for this large inter-individual variability treatment outcomes are not fully understood. Important factors that influence drug dose, response and side effects include age, gender, patient compliance, clinical symptoms, co-morbidities, lifestyle, ancestry and genetic factors. In this context, first strategies using pharmacogenetic (PGx) information bear the promise to optimize medication treatment in clinical practice. State-of-the-art summaries of key concepts and strategies of psychiatric PGx need to consider: 1) Reviews of the evidence, clinical utility and studies including randomized clinical trials of distinct gene-drug pairs; 2) Discussion of current expert recommendations (e.g., Clinical Pharmacogenomics Implementation Consortium); 3) How PGx information can be best used in clinical practice, in particular to avoid pseudo-resistance for antidepressants 3) Highlighting ongoing implementation efforts and 5) providing practical support for psychiatrists and pharmacologists.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 teacher head, 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".