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 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 | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".