Milk Volume at 2 Weeks Predicts Mother's Own Milk Feeding at Neonatal Intensive Care Unit Discharge for Very Low Birthweight Infants
Bibliographic record
Abstract
OBJECTIVE: This study sought to determine the maternal prepregnancy, pregnancy, and delivery risk factors that predicted coming to volume (CTV; achieving pumped mother's own milk [MOM] volume ≥500 mLs/day) and the continuation of MOM provision through to discharge from the neonatal intensive care unit (NICU) in mothers and their very low birthweight (VLBW; <1,500 g at birth) infants. STUDY DESIGN: Secondary analysis of prospectively collected data from 402 mothers of VLBW infants admitted to an urban NICU, including detailed MOM pumping records for a subset (51%) of the cohort. Analyses included inverse probability weighting, multivariate regression, and chi-square statistics. RESULTS: In this high-risk cohort (51.2% black, 27.1% Hispanic, 21.6% white/Asian; 72.6% low income; 61.4% overweight/obese prepregnancy), CTV by day 14 was the strongest predictor of MOM feeding at NICU discharge (odds ratio [OR] 9.70 confidence interval [95% CI] 3.86-24.38, p < 0.01.). Only 39.5% of mothers achieved CTV by postpartum day 14, an outcome that was predicted by gestational age at delivery (OR 1.41, 95% CI 1.15-1.73, p < 0.01), being married (OR 3.66, 95% CI 1.08-12.39, p = 0.04), black race (OR 7.70, 95% CI 2.05-28.97, p < 0.01), cesarean delivery (OR 0.22, 95% CI 0.08-0.63, p = 0.01), and chorioamionitis (OR 0.14, 95% CI 0.02-0.82, p = 0.03). CONCLUSION: Continued provision of MOM at NICU discharge can be predicted in the first 14 postpartum days on the basis of achievement of CTV. We posit that CTV can serve as a quality indicator for improving MOM feedings in the NICU and that lactation support resources should target this early critical postbirth period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".