Proactive Lactation Care is Associated With Improved Outcomes in a Referral NICU
Bibliographic record
Abstract
Background Mother’s milk improves outcomes. Referral neonatal intensive care units face unique lactation challenges with maternal–infant separation and maternal pump dependency. Little is known about lactation resource allocation in this high-risk population. Research Aims To determine differences in human milk outcomes, (1) the proportion of infants fed exclusive or any mother’s milk and (2) recorded number and volume of pumped mothers’ milk bottles, between two models of lactation care in a referral neonatal intensive care unit. Methods This retrospective, longitudinal, two-group comparison study utilized medical record individual feeding data for infants admitted at ≤ Day 7 of age and milk room storage records from reactive and proactive care model time periods (April, 2017–March, 2018; May, 2018–April, 2019). The reactive care model ( n = 509 infants, 58% male, median birth weight and gestational age of 37 weeks,) involved International Board Certified Lactation Consultant referral for identified lactation problems; whereas, the proactive model ( n = 472 infants, 56% male, median birth weight and gestational age 37 weeks) increased International Board Certified Lactation Consultant staffing, who then saw all admissions. Comparisons were performed using chi square, Mann Whitney, and t- tests. Results A proactive lactation approach was associated with an increase in the receipt of any mother’s milk from 74.3% to 80.2% ( p = .03) among participants in the proactive model group. Additionally, their milk room mean monthly bottle storage increased from 5153 ( SD 788) to 6620 ( SD 1314) bottles ( p < .01). Conclusions In this retrospective study at a tertiary referral neonatal intensive care unit, significant improvement inhuman milk outcomes suggests that increased resources for proactive lactation care may improve mother’s milk provision for a high-risk population.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".