Recommendations for pharmacogenetic testing in clinical practice guidelines in the US
Bibliographic record
Abstract
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.
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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 | 0.084 | 0.325 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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.
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".