Opioid Therapy Pharmacogenomics for Noncancer Pain: Efficacy, Adverse Events, and Costs
Bibliographic record
Abstract
Chronic non-cancer pain is a debilitating condition associated with high individual and societal costs. While opioid treatment for pain has been available for centuries, it is associated with high variability in outcome, and a considerable proportion of patients is unable to attain relief from symptoms while suffering adverse events and developing medication dependence. We performed a review of the efficacy of pharmacogenomic markers and their abilities to predict adverse events, dependence, and associated economic costs, focusing on two genes: OPRM1 and CYP2D6. Data sources were articles indexed by PubMed on or before August 6, 2013. Articles were first selected after review of their titles and abstracts, and full papers were read to confirm eligibility. Initially, fifty-two articles were identified. Of these, 17 were relevant to biological actions of pharmacogenomic markers and their effect on therapeutic efficacy, 16 to adverse events, 15 to opioid dependence, and eight to economic costs. In conclusion, increasing costs of opioid therapy have made the advances in pharmacogenomics an attractive solution to personalize care with unclear repercussions related to the impact on costs, morbidity, and outcomes. This intersection of pharmacoeconomics and pharmacogenomics presents a unique platform to further examine current advances in clinical medicine and their utility in cost-effective treatment of chronic pain.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".