Early postnatal weight changes in opioid-exposed infants managed using the Finnegan Neonatal Abstinence Scoring System Versus Eat, Sleep, Console
Bibliographic record
Abstract
Background Despite the many clinical benefits of Eat, Sleep, Console (ESC) to manage infants with neonatal abstinence syndrome (NAS), a recent article theorized that ESC may place infants at an increased risk of excessive postnatal weight loss. The objective of this study was to compare weight changes in the early postnatal period among infants exposed to opioids in utero and managed using 1) the Finnegan Neonatal Abstinence Scoring System (FNASS) or 2) ESC. Methods Pre-post analysis of medical records for opioid-exposed infants born July 1, 2017-May 31, 2023. Type of feeding (exclusive breastfeeding, formula only, combination) and weight changes were compared between the FNASS (n = 45) and ESC (n = 25) groups. Results Type of feeding differed significantly between the groups during the first 5 days of life and at discharge: higher proportions of the FNASS group were exclusively breastfed and higher proportions of the ESC group received formula. There were no significant differences in daily weight changes over the first 5 days of life when we controlled for type of feeding. The average daily weight change during hospitalization did not differ significantly between the two groups (FNASS: median = −53.3 grams/day [IQR: −65.2, −40.8]; ESC: median = −45.9 [IQR: −57.8, −25.5]; p = 0.19). Conclusions We found no evidence of excessive weight loss in the early postnatal period among opioid-exposed infants managed using ESC as compared to FNASS. However, our findings suggest that breastfeeding should be more actively promoted and supported when centers transition from the FNASS model to ESC.
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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 | 0.001 | 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".