Pharmacogenetics of alcohol, nicotine and drug addiction treatments
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
The numerous premature deaths, medical complications and socio-economic repercussions of drug and alcohol addiction suggest that improvements in treatment strategies for addictive disorders are warranted. The use of pharmacogenetics to predict response to medication, side effects and appropriate dosages is relatively new in the field of drug addiction. However, increasing our understanding of the genetic factors influencing these processes may improve the treatment of addiction in the future. We examined the available scientific literature on pharmacogenetic advancements in the field of drug addiction with a focus on alcohol and tobacco to provide a summary of genes implicated in the effectiveness of pharmacotherapy for addiction. In addition, we reviewed pharmacogenetic research on cocaine and heroin dependence. Thus far, the most promising results were obtained for polymorphisms in the OPRM1 and CYP2A6 genes, which have been effective in predicting clinical response to naltrexone in alcoholism and nicotine replacement therapy in smoking, respectively. Opinions differ as to whether pharmacogenetic testing should be implemented in the clinic at this time because clinical utility and cost-effectiveness require further investigation. However, the data summarized in this review demonstrate that pharmacogenetic factors play a role in response to addiction pharmacotherapy and have the potential to aid in the personalization of addiction treatments. Such data may lead to improved cessation rates by allowing physicians to select medications for individuals based, at least in part, on genetic factors that predispose to treatment success or failure rather than on a trial and error basis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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