CYP2D6 Polymorphisms and Codeine Analgesia in Postpartum Pain Management: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Codeine, a common opiate prescribed for pain postcesarean section (c-section), is biotransformed by the highly polymorphic Cytochrome P450 enzyme 2D6 (CYP2D6). Ultrarapid metabolizers (UMs), individuals with multiple active copies of CYP2D6, can biotranform up to 50% more codeine into morphine than normal individuals can. In contrast, poor metabolizers (PMs), individuals who have no active CYP2D6 genes, convert almost no codeine into morphine and as a result may take multiple doses of codeine without attaining analgesia. OBJECTIVE: The aim was to study the relationship between CYP2D6 genotype and codeine analgesia among women recovering from c-section. METHODS: Forty-five mothers prescribed codeine provided a blood sample for CYP2D6 genotyping and recorded their pain level 4 times a day for 3 days immediately after a c-section. Codeine was used on an as-needed basis; doses and times were recorded. The relationship between CYP2D6 genotype, pain scores, need for codeine, and adverse events was studied. Theoretical morphine dose, based on CYP2D6 genotype, was estimated. RESULTS: Women at the genotypic extremes reported codeine effects consistent with their genotype: the 2 PMs of codeine reported no analgesia as a result of taking codeine, whereas 2 of the 3 UMs reported immediate pain relief from codeine but stopped taking it due to dizziness and constipation. Much larger numbers are needed to study similar correlations among extensive and intermediate metabolizers. CONCLUSIONS: In this pilot study, the extreme CYP2D6 genotypes (PMs and UMs) seemed to predict pain response and adverse events. Larger sample sizes are needed to correlate the range of genotypes with pain response.
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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 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".