The pharmacogenetics of opioid treatment for pain management
Bibliographic record
Abstract
BACKGROUND: Opioids are widely used as an analgesic for the treatment of moderate to severe pain. However, there are interindividual variabilities in opioid response. Current evidence suggests that these variabilities can be attributed to single nucleotide polymorphisms in genes involved in opioid pharmacodynamics and pharmacokinetics. Knowledge of these genetic factors through pharamacogenetic (PGx) testing can help clinicians to more consistently prescribe opioids that can provide patients with maximal clinical benefit and minimal risk of adverse effects. AIM: The research outlined in this literature review identifies variants involved in opioid PGx, which may be an important tool to achieving the goal of personalized pain management. RESULTS: ) are all important genes involved in opioid drug response, side effect profile and risk of dependence; these are important genetic factors that should be included in potential opioid PGx tests for pain management. CONCLUSIONS: Employing a PGx-guided strategy for prescribing opioids can improve response rate, reduce side effects and increase adherence to treatment plans for pain; more research is needed to explore opioid-related PGx factors for the development and validation of an opioid genetic panel. Optimal prescriptions could also provide healthcare payers with beneficial savings, while reducing the risk of propagating the current opioid crisis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".