Life‐Threatening Adverse Events Following Therapeutic Opioid Administration in Adults: Is Pharmacogenetic Analysis Useful?
Bibliographic record
Abstract
BACKGROUND: Systemic approaches are needed to understand how variations in the genes associated with opioid pharmacokinetics and response can be used to predict patient outcome. The application of pharmacogenetic analysis to two cases of life-threatening opioid-induced respiratory depression is presented. The usefulness of genotyping in the context of these cases is discussed. METHODS: A panel of 20 functional candidate polymorphisms in genes involved in the opioid biotransformation pathway (CYP2D6, UGT2B7, ABCB1, OPRM1, COMT) were genotyped in these two patients using commercially available genotyping assays. RESULTS: In case 1, the patient experienced adverse outcomes when administered codeine and morphine, but not hydromorphone. Genetic test results suggested that this differential response may be due to an inherent propensity to generate active metabolites from both codeine and morphine. These active metabolites are not generated with hydromorphone. In case 2, the patient experienced severe respiratory depression during postoperative recovery following standard doses of morphine. The patient was found to carry genetic variations that result in decreased morphine efflux transporter activity at the blood-brain barrier and increased sensitivity to opioids. CONCLUSIONS: Knowledge of the relative contribution of pharmacogenetic biomarkers and their influence on opioid response are continually evolving. Pharmacogenetic analysis, together with clinical history, has the potential to provide mechanistic insight into severe respiratory depressive events in patients who receive opioids at therapeutic doses.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".