Milk Biomarkers of Secretory Activation in Breast Pump-Dependent Mothers of Preterm Infants: An Integrative Review
Bibliographic record
Abstract
Background: Lack of mother's own milk (MOM) at discharge from the neonatal intensive care unit (NICU) is a global problem and is often attributable to inadequate MOM volume. Evidence suggests that the origins of this problem are during the first 14 days postpartum, a time period that includes secretory activation (SA; lactogenesis II, milk coming in ). Objectives: To describe and summarize evidence regarding use of MOM biomarkers (MBMs) as a measure of SA in pump-dependent mothers of preterm infants in the NICU and to identify knowledge gaps requiring further investigation. Methods: An integrative review was conducted using Whittemore and Knafl methodology incorporating the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist. A search using electronic databases MEDLINE (through PubMed) and CINAHL (Cumulative Index to Nursing and Allied Health Literature) and reference lists of included articles was conducted. Results: Of the 40 articles retrieved, 6 met the criteria for inclusion. Results revealed the following five findings: (1) Achievement of SA defined by MBMs is delayed and/or impaired in mothers of preterm infants. (2) MBMs are associated with pumped MOM volume. (3) Achievement of SA defined by MBMs is associated with pumping frequency. (4) Delayed and/or impaired achievement of SA defined by MBMs may be exacerbated by maternal comorbidities. (5) There is a lack of consensus as to which MBM(s) and analysis techniques should be used in research and practice. Conclusions: MBMs hold tremendous potential to document and monitor achievement of SA in mothers of preterm infants, with multiple implications for research and clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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 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".