Pain Management Using Clinical Pharmacy Assessments With and Without Pharmacogenomics in an Oncology Palliative Medicine Clinic
Bibliographic record
Abstract
PURPOSE: Approximately 30% of patients with cancer who have pain have symptomatic improvement within 1 month using conventional pain management strategies. Engaging clinical pharmacists in palliative medicine (PM) and use of pharmacogenomic testing may improve cancer pain management. METHODS: Adult patients with cancer with uncontrolled pain had baseline assessments performed by PM providers using the Edmonton Symptom Assessment Scale. Pharmacotherapy was initiated or modified accordingly. A subset of patients consented to pharmacogenomic testing. The first pharmacy assessment occurred within 1 week of baseline and a second assessment was done within another week if intervention was required. Each patient’s final visit was at 1 month. Pain improvement rate (a reduction of two or more points on a 0-to-10 pain scale) from baseline to final visit was compared applying the Fisher exact test to published historical control data, and between patients with and without pharmacogenomic testing. Multivariate logistic regression identified pain improvement covariates. RESULTS: Of 142 patients undergoing pharmacy assessments, 53% had pain improvement compared with 30% in historical control subjects ( P < .001). Pain improvement was not different between those who received (n = 43) and did not receive (n = 99) pharmacogenomics testing (56% v 52%; P = .716). However, of 15 patients with an actionable genotype, 73% had pain improvement. Higher baseline pain (odds ratio [OR], 1.79; 95% CI, 1.43 to 2.24; P < .001), black or other race (OR, 0.42; 95% CI, 0.18 to 0.95; P = .04), and performance status 3 or 4 (OR, 0.18; 95% CI, 0.04 to 0.83; P = .03) were associated with odds of pain improvement, but pharmacogenomic testing was not ( P = .64). CONCLUSION: Including pharmacists in PM improves pain management effectiveness. Although pharmacogenomics did not statistically improve pain, a subset of patients with actionable genotypes may have benefited, warranting larger and randomized studies.
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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 | 0.006 | 0.001 |
| 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 | 0.000 |
| 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".