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Record W4395022322 · doi:10.1093/ajhp/zxae110

Recommendations for pharmacogenetic testing in clinical practice guidelines in the US

2024· review· en· W4395022322 on OpenAlexaff
Daniel L. Hertz, Chad Bousman, Howard L. McLeod, Andrew A. Monte, Deepak Voora, Lori A. Orlando, Rustin D. Crutchley, Benjamin Brown, Wrenda Teeple, Sara Rogers, Jai N. Patel

Bibliographic record

VenueAmerican Journal of Health-System Pharmacy · 2024
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsUniversity of Calgary
FundersNational Institute of General Medical SciencesNational Institutes of Health
KeywordsPharmacogeneticsClinical PracticeMedical physicsMedicinePsychologyEngineering ethicsFamily medicineEngineeringBiologyGeneticsGenotype

Abstract

fetched live from OpenAlex

PURPOSE: Pharmacogenetic testing can identify patients who may benefit from personalized drug treatment. However, clinical uptake of pharmacogenetic testing has been limited. Clinical practice guidelines recommend biomarker tests that the guideline authors deem to have demonstrated clinical utility, meaning that testing improves treatment outcomes. The objective of this narrative review is to describe the current status of pharmacogenetic testing recommendations within clinical practice guidelines in the US. SUMMARY: Guidelines were reviewed for pharmacogenetic testing recommendations for 21 gene-drug pairs that have well-established drug response associations and all of which are categorized as clinically actionable by the Clinical Pharmacogenetics Implementation Consortium. The degree of consistency within and between organizations in pharmacogenetic testing recommendations was assessed. Relatively few clinical practice guidelines that provide a pharmacogenetic testing recommendation were identified. Testing recommendations for HLA-B*57:01 before initiation of abacavir and G6PD before initiation of rasburicase, both of which are included in drug labeling, were mostly consistent across guidelines. Gene-drug pairs with at least one clinical practice guideline recommending testing or stating that testing could be considered included CYP2C19-clopidogrel, CYP2D6-codeine, CYP2D6-tramadol, CYP2B6-efavirenz, TPMT-thiopurines, and NUDT15-thiopurines. Testing recommendations for the same gene-drug pair were often inconsistent between organizations and sometimes inconsistent between different guidelines from the same organization. CONCLUSION: A standardized approach to evaluating the evidence of clinical utility for pharmacogenetic testing may increase the inclusion and consistency of pharmacogenetic testing recommendations in clinical practice guidelines, which could benefit patients and society by increasing clinical use of pharmacogenetic testing.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.084
metaresearch head score (Gemma)0.325
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.084
Threshold uncertainty score0.446

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0840.325
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0090.009
Science and technology studies0.0030.003
Scholarly communication0.0060.006
Open science0.0050.005
Research integrity0.0110.012
Insufficient payload (model declined to judge)0.0100.004

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.

Opus teacher head0.580
GPT teacher head0.661
Teacher spread0.080 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations22
Published2024
Admission routes1
Has abstractyes

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