Potential NICU Environmental Influences on the Neonate's Microbiome
Bibliographic record
Abstract
OBJECTIVE: To identify how the neonatal intensive care unit (NICU) environment potentially influences the microbiome high-risk term and preterm infants. DATA SOURCES: Electronic databases utilized to identify studies published in English included PubMed, Google Scholar, Cumulative Index for Nursing and Allied Health Literature, and BioMedSearcher. Date of publication did not limit inclusion in the review. STUDY SELECTION: Two hundred fifty articles were assessed for relevance to the research question through title and abstract review. Further screening resulted in full review of 60 articles. An in-depth review of all 60 articles resulted in 39 articles that met inclusion criteria. Twenty-eight articles were eliminated on the basis of the type of study and subject of interest. DATA EXTRACTION: Studies were reviewed for information related to environmental factors that influence microbial colonization of the neonatal microbiome. Environment was later defined as the physical environment of the NICU and nursery caregiving activities. DATA SYNTHESIS: Studies were characterized into factors that impacted the infant's microbiome—parental skin, feeding type, environmental surfaces and caregiving equipment, health care provider skin, and antibiotic use. CONCLUSIONS: Literature revealed that various aspects of living within the NICU environment do influence the microbiome of infants. Caregivers can implement strategies to prevent environment-associated nosocomial infection in the NICU such as implementing infection control measures, encouraging use of breast milk, and decreasing the empirical use of antibiotics.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".