Economic evaluations of human milk for very preterm infants: a systematic review
Bibliographic record
Abstract
Background Very preterm infants are highly vulnerable to complications, imposing a significant economic burden on healthcare systems. Human milk has protective effects on these infants, but there is no systematic review on its economic impact. Objective We conducted a comprehensive review of studies assessing the economic evaluations of human milk for very preterm infants. Methods Our literature search covered PubMed, Embase, the Cochrane Library, and Web of Science. Two reviewers independently extracted data on economic evaluations and assessed study quality using the Pediatric Quality Appraisal Questionnaire (PQAQ). Results Fourteen studies of moderate quality, conducted in the United States, Germany, and Canada, met the inclusion criteria. However, the studies analyzed had notable variations and shortcomings. The majority of these studies (n = 11) performed economic evaluations from a healthcare system perspective, utilizing cost-consequence analysis (n = 6) up to the point of neonatal discharge (n = 11). All human milk interventions indicated cost-effective or cost saving results; only a minority included discounting (n = 2). Conclusion This systematic review suggests that economic evaluation of human milk for very preterm infants is an expanding area of research. Human milk for very preterm infants offers substantial economic advantages during neonatal intensive care unit hospitalization. Standardized and high-quality studies are needed to determine the cost-effectiveness of human milk for very preterm infants in the future. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO , identifier (CRD42024539574).
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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 | 0.017 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".