Exploring the development of a home nasogastric feeding program to facilitate early discharge of infants from the NICU
Bibliographic record
Abstract
Keywords: Nasogastric, Neonatal Discharge of infants from the neonatal intensive care unit (NICU) depends on the growth and ability of the neonate to feed orally. However, achieving these goals may cause significant stress on families, prolong discharge timing, and strain hospital resources. NICU's in other countries have shown success in early discharging before full oral feeding using a nasogastric feeding tube. Home gavage feeding, however, is not common in Canada due to multiple reasons including a lack of resources. Windsor Regional Hospital's NICU has the capacity to implement such a program offering out-patient follow-ups to closely monitor feeding and growth, as well out-patient dietician expertise for nutritional support. To ensure the safety and efficacy before implementing the program, a retrospective chart review will be conducted to determine the emergency room visit and readmission rates of neonates discharged with an NG-tube. Additionally, a prospective cohort will be surveyed to assess the impact of the program on family stress and their quality of life. This study will determine the feasibility of this program as well as short and long term impacts of the home gavage feeding on neonates and their families. Doing so will facilitate local implementation of the study, decreasing hospital stays, resource consumption, and family strain.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".