An observational study of associations among maternal fluids during parturition, neonatal output, and breastfed newborn weight loss
Bibliographic record
Abstract
BACKGROUND: Newborn weight measurements are used as a key indicator of breastfeeding adequacy. The purpose of this study was to explore non-feeding factors that might be related to newborn weight loss. The relationship between the intravenous fluids women receive during parturition (the act of giving birth, including time in labour or prior to a caesarean section) and their newborn's weight loss during the first 72 hours postpartum was the primary interest. METHODS: In this observational cohort study, we collected data about maternal oral and IV fluids during labour or before a caesarean section. Participants (n = 109) weighed their newborns every 12 hours for the first three days then daily to Day 14, and they weighed neonatal output (voids and stools) for three days. RESULTS: At 60 hours (nadir), mean newborn weight loss was 6.57% (SD 2.51; n = 96, range 1.83-13.06%). When groups, based on maternal fluids, were compared (≤1200 mls [n = 21] versus > 1200 [n = 53]), newborns lost 5.51% versus 6.93% (p = 0.03), respectively. For the first 24 hours, bivariate analyses show positive relationships between a) neonatal output and percentage of newborn weight lost (r(96) = 0.493, p < 0.001); and b) maternal IV fluids (final 2 hours) and neonatal output (r(42) = 0.383, p = 0.012). At 72 hours, there was a positive correlation between grams of weight lost and all maternal fluids (r(75) = 0.309, p = 0.007). CONCLUSIONS: Timing and amounts of maternal IV fluids appear correlated to neonatal output and newborn weight loss. Neonates appear to experience diuresis and correct their fluid status in the first 24 hours. We recommend a measurement at 24 hours, instead of birth weight, for baseline when assessing weight change. Because practices can differ between maternity settings, we further suggest that clinicians should collect and analyze data from dyads in their care to determine an optimal baseline measurement.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".