Introducing pharmacogenetic testing with clinical decision support into primary care: a feasibility study
Bibliographic record
Abstract
BACKGROUND: Inappropriate prescribing increases patient illness and death owing to adverse drug events. The inclusion of genetic information into primary care medication practices is one solution. Our aim was to assess the ability to obtain and genotype saliva samples and to determine the levels of use of a decision support tool that creates medication options adjusted for patient characteristics, drug-drug interactions and pharmacogenetics. METHODS: We conducted a cohort study in 6 primary care settings (5 family practices and 1 pharmacy), enrolling 191 adults with at least 1 of 10 common diseases. Saliva samples were obtained in the physician's office or pharmacy and sent to our laboratory, where DNA was extracted and genotyped and reports were generated. The reports were sent directly to the family physician/pharmacist and linked to an evidence-based prescribing decision support system. The primary outcome was ability to obtain and genotype samples. The secondary outcomes were yield and purity of DNA samples, ability to link results to decision support software and use of the decision support software. RESULTS: Genotyping resulted in linking of 189 patients (99%) with pharmacogenetic reports to the decision support program. A total of 96.8% of samples had at least 1 actionable genotype for medications included in the decision support system. The medication support system was used by the physicians and pharmacists 236 times over 3 months. INTERPRETATION: ClinicalTrials.gov, NCT02383290.
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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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".