Pharmacogenetics of chronic pain management
Bibliographic record
Abstract
OBJECTIVE: The experience of chronic pain is one of the commonest reasons individuals seek medical attention, making the management of chronic pain a major issue in clinical practice. Drug metabolism and responses are affected by many factors, with genetic variations offering only a partial explanation of an individual's response. There is a paucity of evidence for the benefits of pharmacogenetic testing in the context of pain management. DESIGN AND METHODS: We reviewed the literature between 2000 and 2013, and references cited therein, using various keywords related to pain management, pharmacology and pharmacogenetics. RESULTS: Opioids continue to be the mainstay of chronic pain management. Several non-opioid based therapies, such as treatment with cannabinoids, gene therapy and epigenetic-based approaches are now available for these patients. Adjuvant therapies with antidepressants, benzodiazepines or anticonvulsants can also be useful in managing pain. Currently, laboratory monitoring of pain management patients, if performed, is largely through urine drug measurements. CONCLUSIONS: Drug half-life calculations can be used as functional markers of the cumulative effect of pharmacogenetics and drug-drug interactions. Assessment of half-life and therapeutic effects may be more useful than genetic testing in preventing adverse drug reactions to pain medications, while ensuring effective analgesia. Definitive, mass spectrometry-based methods, capable of measuring parent drug and metabolite levels, are the most useful assays for this purpose. Urine drug measurements do not necessarily correlate with serum drug concentrations or therapeutic effects. Therefore, they are limited in their use in monitoring efficacy and toxicity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".