Neonatal neurobehavior effects following buprenorphine versus methadone exposure
Bibliographic record
Abstract
AIM: To determine the effects of in utero exposure to methadone or buprenorphine on infant neurobehavior. DESIGN: Three sites from the Maternal Opioid Treatment: Human Experimental Research (MOTHER) study, a double-blind, double-dummy, randomized clinical trial participated in this substudy. SETTING: Medical Centers that provided comprehensive maternal care to opioid-dependent pregnant women in Baltimore, MD, Providence, RI and Vienna, Austria. PARTICIPANTS: Thirty-nine full-term infants. MEASUREMENTS: The Neonatal Intensive Care Unit (NICU) Network Neurobehavioral Scale (NNNS) was administered to a subgroup of infants on postpartum days 3, 5, 7, 10, 14-15 and 28-30. FINDINGS: While neurobehavior improved for both medication conditions over time, infants exposed in utero to buprenorphine exhibited fewer stress-abstinence signs (P < 0.001), were less excitable (P < 0.001) and less over-aroused (P < 0.01), exhibited less hypertonia (P < 0.007), had better self-regulation (P < 0.04) and required less handling (P < 0.001) to maintain a quiet alert state relative to in utero methadone-exposed infants. Infants who were older when they began morphine treatment for withdrawal had higher self-regulation scores (P < 0.01), and demonstrated the least amount of excitability (P < 0.02) and hypertonia (P < 0.02) on average. Quality of movement was correlated negatively with peak NAS score (P < 0.01), number of days treated with morphine for NAS (P < 0.01) and total amount of morphine received (P < 0.03). Excitability scores were related positively to total morphine dose (P < 0.03). CONCLUSION: While neurobehavior improves during the first month of postnatal life for in utero agonist medication-exposed neonates, buprenorphine exposure results in superior neurobehavioral scores and less severe withdrawal than does methadone exposure.
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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 | 0.001 |
| 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 | 0.000 |
| 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".