Improving prescribing: a feasibility study of pharmacogenetic testing with clinical decision support in primary healthcare in Singapore
Bibliographic record
Abstract
BACKGROUND: The study of genetic variation as a factor influencing drug safety, efficacy, and effectiveness has brought about significant breakthroughs in understanding the clinical application of gene-drug interactions to better manage drug therapy. OBJECTIVE: This study was designed to assess the feasibility of collecting buccal samples by general practitioners (GPs) at private practices in Singapore within a usual consultation, incorporating use of a pharmacogenetics-based medical decision support system to guide subsequent drug dosing. METHODS: We used a prospective cohort study design, with GPs recruiting 189 patients between October 2020 and March 2021. The genotypes of 51 biallelic SNPs were determined using Illumina Infinium Global Screening Array. RESULTS: Seven GPs from 6 private practices recruited and obtained buccal samples from a total of 189 patients. All patients had at least one actionable variant. The prevalence of patients having 2, 3, or 4 variants was 37.0%, 32.8%, and 12.7%, respectively. Potential alterations to medications were identified using the Clinical Decision Support System. Patients were accepting and the GPs were enthusiastic about the potential of pharmacogenetics to personalize medicine for their patients. CONCLUSION: This is the first study in Singapore to demonstrate the feasibility of pharmacogenetic testing in primary care. The high prevalence of genetic variants underscores the potential use of pharmacogenetics in this setting.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".