Is Pharmacogenetic Testing a Vital Tool for Enhancing Therapeutic Management of Patients Worldwide?
Bibliographic record
Abstract
Clinical pharmacology is a discipline that includes education, research, and the implementation of knowledge into clinical practice, which ranges from precision dosing to therapeutic drug monitoring, and most recently, to the implementation of pharmacogenetic/pharmacogenomic (PGx) testing services to precisely administer drugs based on an individual's genetic make-up.In fact, PGx has become one of the core scientific pillars of the American Society for Clinical Pharmacology and Therapeutics (ASCPT) and its flagship journal, Clinical Pharmacology & Therapeutics (CPT).PGx implementation services have been rapidly adopted in academic healthcare centers throughout the United States and in Europe.These services are grounded in the availability of new genetic technologies and a wealth of scientific discoveries, generally describing the influence of genetic variants on drug responses in European ancestral populations.With the availability of PGx information, the Clinical Pharmacogenetics Implementation Consortium (CPIC) was established to develop guidelines on drug and dose selection for individuals based on their genetic information.1 These guidelines, published in CPT, 2,3 are increasingly being incorporated into clinical decision support systems, and used to advise providers on how to use PGx information in drug or dose selection.1 However, despite of their widespread adoption in academic medical centers, there remains a resistance to PGx testing among healthcare providers.This can be attributed to various factors, such as cost of testing, requirements for expensive infrastructure, lack of provider education, and skepticism that there is any major benefit of testing.4 With the goal of stimulating discussion among clinical pharmacologists and others, the editors of CPT sponsored a session at the 2024 annual meeting of ASCPT, entitled: "Is pharmacogenetic testing a vital tool for enhancing therapeutic management of patients worldwide?"(Colorado Springs CO, March 28, 2024).The session, which was moderated by CPT Editor-in-Chief, Piet van der Graaf, and Deputy Editor, Kathleen Giacomini, included four clinical pharmacologists or geneticists who brought different types of expertise to the panel
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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 | 0.017 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".