How community pharmacy pharmacogenomics testing services around the world can inform their design and delivery in the UK
Bibliographic record
Abstract
Introduction: Pharmacogenomics (PGx) reduces the need for 'trial-and-error' prescribing and the chances of adverse reactions, and improves patient outcomes. With the cost of PGxtesting falling rapidly, in line with the cost of other testing within the NHS, it is already being deployed by community pharmacists outside the UK. Aim: To learn from experiences of PGx delivery in community pharmacies in other countries to inform the set up and design of future UK pharmacy services. Method: A five-stage scoping review methodological framework was deployed. The research question was identified and the relevant studies were selected from databases, using the search terms'pharmacogenomics' OR 'pharmacogenetics' AND 'community pharmacy'. A data-extraction tool was used to collect the data, which was subsequently charted into categories, including barriers, enablers, patient-orientated outcomes and recommendations for future research. The results were then collated, summarised and reported. Results: From the 15 papers reviewed, it was noted that community pharmacy-based PGx services are becoming increasingly widespread, having been implemented in the United States, Canada, the Netherlands and Cyprus. Enablers for implementation of a PGx testing service in a community pharmacy setting included patient interest, pharmacist willingness and confidence to deliver the service, the service being comparable to existing pharmacy services (e.g. vaccination programmes) and prescriber acceptance of the results. Barriers included education and training of pharmacists, access to appropriate clinical resources, lack of patient-friendly resources and time capacity. Conclusion: Community pharmacy-led PGx services have been reportedin several different countries. For such services to work well, they need patient interest, pharmacist engagement and training, available supporting information for pharmacists and prescriber acceptance of recommendations for any changes to patient prescriptions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".