Predicting treatment for neonatal abstinence syndrome in infants born to women maintained on opioid agonist medication
Bibliographic record
Abstract
AIM: To identify factors that predict the expression of neonatal abstinence syndrome (NAS) in infants exposed to methadone or buprenorphine in utero. DESIGN AND SETTING: Multi-site randomized clinical trial in which infants were observed for a minimum of 10 days following birth, and assessed for NAS symptoms by trained raters. PARTICIPANTS: A total of 131 infants born to opioid dependent mothers, 129 of whom were available for NAS assessment. MEASUREMENTS: Generalized linear modeling was performed using maternal and infant characteristics to predict: peak NAS score prior to treatment, whether an infant required NAS treatment, length of NAS treatment and total dose of morphine required for treatment of NAS symptoms. FINDINGS: Of the sample, 53% (68 infants) required treatment for NAS. Lower maternal weight at delivery, later estimated gestational age (EGA), maternal use of selective serotonin re-uptake inhibitors (SSRIs), vaginal delivery and higher infant birthweight predicted higher peak NAS scores. Higher infant birthweight and greater maternal nicotine use at delivery predicted receipt of NAS treatment for infants. Maternal use of SSRIs, higher nicotine use and fewer days of study medication received also predicted total dose of medication required to treat NAS symptoms. No variables predicted length of treatment for NAS. CONCLUSIONS: Maternal weight at delivery, estimated gestational age, infant birthweight, delivery type, maternal nicotine use and days of maternal study medication received and the use of psychotropic medications in pregnancy may play a role in the expression of neonatal abstinence syndrome severity in infants exposed to either methadone or buprenorphine.
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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 | 0.005 |
| 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".