Fetal assessment before and after dosing with buprenorphine or methadone
Bibliographic record
Abstract
AIM: To determine pre- and post-dosing effects of prenatal methadone compared to buprenorphine on fetal wellbeing. DESIGN: A secondary analysis of data derived from the Maternal Opioid Treatment: Human Experimental Research (MOTHER) study, a double-blind, double-dummy, randomized clinical trial. SETTING: Six United States sites and one European site that provided comprehensive opioid-dependence treatment to pregnant women. PARTICIPANTS: Eighty-one of the 131 opioid-dependent pregnant women completing the MOTHER clinical trial, assessed between 31 and 33 weeks of gestation. MEASUREMENTS: Two fetal assessments were conducted, once before and once after study medication dosing. Measures included mean fetal heart rate (FHR), number of FHR accelerations, FHR reactivity in the fetal non-stress test (NST) and biophysical profile (BPP) score. FINDINGS: Significant group differences were found for number of FHR accelerations, non-reactive NST and BPP scores (all Ps < 0.05). There were no significant group differences before time of dosing. Significant decreases (all Ps < 0.05) occurred from pre- to post-dose assessment for mean FHR, FHR accelerations, reactive NST and fetal movement. The decrease in accelerations and reactive NST were significant only for fetuses in the methadone group, and this resulted in a significantly lower likelihood of a reactive NST compared to fetuses in the buprenorphine group. CONCLUSION: Buprenorphine compared with methadone appears to result in less suppression of mean fetal heart rate, fetal heart rate reactivity and the biophysical profile score after medication dosing and these findings provide support for the relative safety of buprenorphine when fetal indices are considered as part of the complete risk-benefit ratio.
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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) | 0.001 | 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) | 0.003 | 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".