<scp> <i>CYP2D6</i> </scp> ‐guided opioid therapy for adults with cancer pain: A randomized implementation clinical trial
Bibliographic record
Abstract
INTRODUCTION: The CYP2D6 enzyme metabolizes opioids commonly prescribed for cancer-related pain, and CYP2D6 polymorphisms may contribute to variability in opioid response. We evaluated the feasibility of implementing CYP2D6-guided opioid prescribing for patients with cancer and reported pilot outcome data. METHODS: Adult patients from two cancer centers were prospectively enrolled into a hybrid implementation-effectiveness clinical trial and randomized to CYP2D6-genotype-guided opioid selection, with clinical recommendations, or usual care. Implementation metrics, including provider response, medication changes consistent with recommendations, and patient-reported pain and symptom scores at baseline and up to 8 weeks, were assessed. RESULTS: Most (87/114, 76%) patients approached for the study agreed to participate. Of 85 patients randomized, 71% were prescribed oxycodone at baseline. The median (range) time to receive CYP2D6 test results was 10 (3-37) days; 24% of patients had physicians acknowledge genotype results in a clinic note. Among patients with CYP2D6-genotype-guided recommendations to change therapy (n = 11), 18% had a change congruent with recommendations. Among patients who completed baseline and follow-up questionnaires (n = 48), there was no difference in change in mean composite pain score (-1.01 ± 2.1 vs. -0.41 ± 2.5; p = 0.19) or symptom severity at last follow-up (3.96 ± 2.18 vs. 3.47 ± 1.78; p = 0.63) between the usual care arm (n = 26) and genotype-guided arm (n = 22), respectively. CONCLUSION: Our study revealed high acceptance of pharmacogenetic testing as part of a clinical trial among patients with cancer pain. However, provider response to genotype-guided recommendations was low, impacting assessment of pain-related outcomes. Addressing barriers to utility of pharmacogenetics results and clinical recommendations will be critical for implementation success.
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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 | 0.016 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".