Standardised feeding regimens: hope for reducing the risk of necrotising enterocolitis
Bibliographic record
Abstract
A perspective on the paper by Patole and de Klerk1 Necrotising enterocolitis (NEC), an acquired gastrointestinal disease in neonatal intensive care unit survivors, affects one to three infants per 1000 live births and is associated with significant mortality and morbidity.2,3 Although it has not been proven, many believe that, in premature infants, a precursor to NEC is feeding intolerance, specifically, prefeed gastric residuals or bile stained aspirates.4–6 These associated intestinal signs of NEC may also reflect a delay in maturation of the neonate’s motor activity such that they lack complete interdigestive cycles during fasting. As no biological markers exist to diagnose NEC, clinical wisdom guides decision making related to its diagnoses and management. Furthermore, there is a paucity of research identifying feeding practices, except for breast milk feeds, that offer the greatest potential benefit against developing NEC. Moreover, hormonal, anatomical, and functional limitations of low birthweight infants, the additive effects of critical illness, and intrauterine environmental factors—for example, antenatal glucocorticoids—complicate feeding decisions in this population of infants. Consequently, there is great variability in feeding orders for low birthweight infants. A standardised feeding regimen (SFR) is one strategy to address the challenges of feeding low birthweight infants. Establishing such an SFR would require synthesising the available evidence7 and communicating the clinical wisdom from the experts, thereby promoting a more systematic approach to feeding low birthweight infants. A systematic review …
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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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".