Pharmacogenomics in primary care: a quality improvement project at Alconbury and Brampton surgeries 2019–2023
Bibliographic record
Abstract
BACKGROUND: Treatment of depression is common in primary care but not every antidepressant is effective in every patient. Adverse drug reactions are common, imposing a substantial burden on the patient and the NHS. Pharmacogenomics (PGx) utilises an individual's genetic makeup to predict their response to medications. By tailoring prescriptions to a person's genetic profile, PGx can significantly reduce adverse drug reactions, identify non-responders to medications, and enhance overall patient outcomes. AIM: To see if PGx testing for antidepressants can be undertaken in general practice as part of 'usual care'; to increase GP and patient awareness of PGx testing; to alter patient treatment according to the results of testing; and to sustain these changes at 4 years. METHOD: In 2019, 23 patients were recruited by GPs at the surgery. They consented and had cheek swabs to check their genetic profile to common antidepressants. Results were reviewed at 1 week by the GP and patient, treatment changes made and reviewed again at 4 years. RESULTS: On the CYP2D pathway, 19/23 patients were extensive (normal) metabolisers, one intermediate and two poor. These two had their treatments changed. At 4-year review 19/23 were on appropriate treatment (two had been stopped) but two had inappropriate drug treatment. On CYP2C19 pathway, 10/23 were normal metabolisers, eight intermediate, zero poor but five ultrarapid (they had their treatment changed). At 4-year review, 20/23 were on appropriate treatment (two stopped) and one inappropriate. CONCLUSION: PGx testing works in primary care, improving patient outcomes sustainably.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".