Perinatal Factors Associated With Breastfeeding Trends After Preterm Birth <29 Weeks Gestation: Can We Predict Early Discontinuation?
Bibliographic record
Abstract
OBJECTIVE: To determine the rates and perinatal factors associated with initiation and early discontinuation of breastfeeding among very preterm neonates. METHODS: This was a retrospective cohort study of very preterm infants (<29 weeks gestation) admitted to 2 regional Level III neonatal intensive care units (NICUs) from January 1, 2015, to December 31, 2019. A national neonatal database was used to evaluate initiation and continuation rates of breastfeeding and associated perinatal factors. Stored nutrition profiles and delivery record books were used to determine feeding volumes associated with continuation of breastfeeding to hospital discharge for a subgroup of infants at a single site. Descriptive and inferential statistics were used to present the results between groups, and logistic regression modeling was used to calculate crude and adjusted odds ratios (OR) and 95% CI. RESULTS: Of 391 eligible neonates, 84% initiated breastfeeding but only 38% continued to discharge. Interestingly, frequency of breastfeeding initiation (P < 0.001) and continuation (P < 0.001) declined over the study period. After adjustment for confounders, younger maternal age, earlier gestational age, cigarette smoking, and multiparity were significantly associated with early discontinuation of breastfeeding prior to hospital discharge. Early discontinuation of breastfeeding was also related to lower volumes of breastmilk by day 7 of life (P = 0.004). CONCLUSION: Very preterm neonates are at high risk for non-initiation and early discontinuation of breastfeeding. The early postnatal period represents a critical time to establish breastmilk volumes, and the identification of key perinatal risk factors allows for early and targeted breastfeeding support.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".