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. Publication History Article published online: 12 March 2024 © 2024. Thieme. All rights reserved. Georg Thieme Verlag Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.047 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.017 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".