Effect of hepatitis C virus status on liver enzymes in opioid‐dependent pregnant women maintained on opioid‐agonist medication
Bibliographic record
Abstract
AIM: To examine hepatic enzyme test results throughout the course of pregnancy in women maintained on methadone or buprenorphine. DESIGN: Participants were randomized to either methadone or buprenorphine maintenance. Blood chemistry tests, including liver transaminases and hepatitis C virus (HCV) status, were determined every 4 weeks and once postpartum. As part of a planned secondary analysis, generalized mixed linear models were conducted with aspartate aminotransferase (AST), alanine aminotransferase (ALT) and gamma-glutamyl transferase (GGT) as the dependent variables. SETTING: Six US sites and one European site that provided comprehensive treatment to pregnant opioid-dependent women. PARTICIPANTS: A total of 175 opioid-dependent pregnant women enrolled in the Maternal Opioid Treatment: Human Experimental Research (MOTHER) study. FINDINGS: ALT, AST and GGT levels decreased for all subjects across pregnancy trimesters, rising slightly postpartum. HCV-positive subjects exhibited higher transaminases at all time-points compared to HCV-negative subjects, regardless of medication (all Ps < 0.05) condition. Both HCV-positive and negative buprenorphine-maintained participants exhibited lower GGT levels than those who were methadone-maintained (P < 0.05). CONCLUSIONS: Neither methadone nor buprenorphine appear to have adverse hepatic effects in the treatment of pregnant opioid-dependent women.
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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 | 0.004 |
| 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) | 0.001 | 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".