A retrospective cohort study of neonatal abstinence syndrome patients following implementation of Eat Sleep Console Protocol
Bibliographic record
Abstract
Background: Neonatal abstinence syndrome (NAS) is a set of withdrawal symptoms experienced in a newborn if they have been exposed to substances such as opioids prior to birth. The incidence of NAS in British Columbia as well as NAS-related healthcare burden, has been increasing within the last several years. Aim: To determine whether the novel Eat, Sleep, Console (ESC) approach to NAS improves outcomes such as length of hospitalization compared to Finnegan Neonatal Abstinence Scoring System (FNASS) approach in the treatment of neonatal patients with NAS admitted to the Neonatal Intensive Care Unit (NICU). Methods: Retrospective paper and electronic chart review of neonatal patients born at ≥35 weeks gestation and ≤28 days of life with prenatal exposure to opioids and admitted to Kelowna General Hospital (KGH) Neonatal Intensive Care Unit (NICU) between January 2018 and February 2023. Results: The primary outcome of hospital length of stay was 19.5 days for the ESC group and 27.5 days for FNASS (p=0.039). Secondary outcomes of total weaning morphine doses (53 vs 147; p<0.001), percentage requiring maintenance dosing (43.8% vs 100%; p<0.001), and length of wean (8.2 vs 18.1 days; p=0.003) were significantly less with ESC. Percentage who received only as needed morphine was greater with ESC (37.5% vs 0%; p<0.001). Total morphine dose (11.2mg vs 22.5mg; p=0.09) and adverse events (6% vs 11.7%; p=0.54) were not statistically significantly different. Conclusions: Compared to FNASS, ESC approach improves several outcomes for NAS patients admitted to the NICU including a reduction in length of hospitalization by 8 days.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".