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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".