83: Management of Infants with Neonatal Abstinence Syndrome
Bibliographic record
Abstract
The rate of neonatal abstinence syndrome (NAS) has risen dramatically in North America, resulting in increased health care resource utilization. Marked variation in management of NAS has been reported in the United States and Europe, but data from Canada are limited. Describe current NAS management practices in NICUs in Canada, including location for infant monitoring (admission to NICU vs. room-in with mother); location for pharmacological treatment; use of cardiorespiratory monitors when pharmacological treatment is initiated; breastfeeding recommendations; and discharge of infants on medication. Qualified personnel in all Level II and Level III NICUs in Canada were emailed to request their participation, with follow-up if no response was received. A medical resident administered a structured, pre-tested telephone survey and data were analyzed using SPSS. Surveys were completed by 65 of 103 units (63.1%). Common practices included having a written NAS practice guideline (92.3%); using the original or modified Finnegan's scoring system to monitor NAS signs (89.5%); using non-pharmacological treatment methods (e.g. dim lights, swaddling) (100%); using morphine as the first-line medication (96.9%); using monitors routinely during pharmacological treatment (93.8%); and admitting infants to the NICU for pharmacological treatment (89.2%). Practices that showed greater variability included where high-risk infants are observed (44.6% in the NICU, 52.3% rooming-in with the mother, 3.1% in either location or on a pediatric ward); use of adjunct medications (65.1% use overall with phenobarbital used most commonly [82.9%] followed by clonidine [41.5%]); breastfeeding practices (53.8% always encourage breastfeeding, 44.6% discourage breastfeeding if the mother is using illicit drugs, and 1.5% make recommendations on a case-by-case basis); and discharge of infants on first-line (36.9%) and adjunct medications (38.9%), where used. While most NICUs surveyed follow similar practices in scoring NAS signs and use of non-pharmacological and first-line pharmacological treatment, there is notable variation in practices related to parent involvement in care, such as maintaining the mother-infant dyad during monitoring and treatment, encouraging breastfeeding, and discharging the infant on medication. This underscores the need for further research that can be translated into best practices.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".