Evaluating antenatal breastmilk expression outcomes: a scoping review
Bibliographic record
Abstract
BACKGROUND: Antenatal breastmilk expression (aBME) is recommended by some healthcare providers to improve lactation, breastfeeding, and newborn outcomes, particularly for women with diabetes as they face unique challenges with breastfeeding. However, there is limited evidence of the potential harms and benefits of this practice. Our objective was to conduct a scoping review to map the literature describing maternal and newborn outcomes of aBME. METHODS: We searched Medline, Embase, CINAHL, Cochrane Database of Systematic Reviews, British Library E-Theses Online Services (EThOS) database, OpenGrey, and Clinical trials.gov from inception to January 2020. Studies in English that reported on the effect of aBME on maternal and newborn outcomes, and the experiences of women who have engaged in the practice were included for screening. Titles, abstracts, and full-text articles were screened by two independent reviewers. A critical appraisal and clinical consultation were conducted. Key findings were extracted and summarized. RESULTS: We screened 659 studies and 20 met the inclusion criteria. The majority of included studies (n = 11, 55.0%) were published after 2015, and seven (35.0%) originated from Australia. Ten (50.0%) studies provided data on high-risk obstetrical populations, including those with diabetes (n = 8), overweight or obesity (n = 1), and preeclampsia (n = 1). Commonly reported outcomes included breastfeeding status at discharge or follow-up, mode of delivery, newborn blood glucose, and time to establishing full lactation. Maternal experiences were related to challenges with aBME, confidence and mastery, perceived impact, security and altruism, learning and resources, and physical symptoms as a result of aBME. The critical appraisal demonstrated limited high-quality evidence surrounding aBME. CONCLUSIONS: Our findings demonstrate increasing interest in the safety, efficacy, and acceptability of aBME. Existing studies are heterogenous with variable research questions, outcomes, study designs, and methodology. The recommendations made in this review can be used to help inform future studies evaluating aBME.
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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 | 0.037 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.031 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".