Eat, Sleep, Console Approach
Bibliographic record
Abstract
BACKGROUND: The opioid epidemic in the United States has resulted in an increased number of drug-exposed infants who are at risk for developing neonatal abstinence syndrome (NAS). Historically, these infants have been treated with the introduction and slow weaning of pharmaceuticals. Recently, a new model called Eat, Sleep, Console (ESC) has been developed that focuses on the comfort and care of these infants by maximizing nonpharmacologic methods, increasing family involvement in the treatment of their infant, and prn or "as needed" use of morphine. PURPOSE: The purpose of this evidenced-based practice brief was to summarize and critically review emerging research on the ESC method of managing NAS and develop a recommendation for implementing an ESC model. METHODS: A literature review was conducted using PubMed, Cochrane, and Google Scholar with a focus on ESC programs developed for treating infants with NAS. FINDING/RESULTS: Several studies were found with successful development and implementation of the ESC model. Studies supported the use of ESC to decrease length of stay, exposure to pharmacologic agents, and overall cost of treatment.Video Abstract Available at https://journals.lww.com/advancesinneonatalcare/Pages/videogallery.aspx?videoId=32&autoPlay=true.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".