A summary of best evidence on breast milk expression of premature infants' mothers in maternal separation
Bibliographic record
Abstract
Objective To retrieve, assess and summarize the best evidence on breast milk expression of premature infants' mothers in maternal separation so as to provide high-quality evidence for medical staff to carry out breast feeding guide. Methods Guidelines, systematic reviews, recommended practices and evidence summaries were retrieved in the UpToDate, WHO, Academy of Breastfeeding Medicine (ABM) , Cochrane Library, Joanna Briggs Institute Library, Registered Nurses' Association of Ontario (RNAO) , National Guideline Clearinghouse (NGC) , PubMed, Embase, Chinese Biological Medicine, Chinese National Knowledge Infrastructure, WanFang data, VIP and the evidence-based nursing database of Fudan University from January 2008 to December 2018. Results A total of 7 literatures were included, two guidelines, one clinical decision, two systematic reviews and two evidence summaries. A total of 19 evidences were gathered in 7 aspects, such as training for medical staff, parents' education, selection of breast pump, preparation before expression, operational skills of breast expression, measures for increasing lactation and expression effects monitor. Conclusions Clinical medical staff should lay down the individualized nursing measures for maternal women to ensure breast milk collected as soon as possible, gain enough breast milk and fill the bill of growth and development of premature infants. Key words: Infant, premature; Breast milk; Expression; Lactation; Mothers
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".