SMOFlipid Impact on Growth and Neonatal Morbidities in Very Preterm Infants
Bibliographic record
Abstract
The soybean oil, medium-chain triglycerides, olive oil, and fish oil lipid (SMOFlipid) is increasingly being used worldwide without definite evidence of its benefits. We examined the effect of SMOFlipid on growth velocity and neonatal morbidities in very preterm infants. Very preterm infants who received soybean-based lipid emulsion between January 2015 and 2018 were compared with those who received SMOFlipids between 2019 and January 2022 in our neonatal tertiary center. Linear regression analysis was conducted to analyze the association between type of lipid emulsion and growth velocity. Modified log-Poisson regression with generalized linear models and a robust variance estimator (Huber−White) were applied to adjust for potential confounding factors. A total of 858 infants met our inclusion criteria. Of them, 238 (27.7%) received SMOFlipid. SMOFlipid was associated with lower growth velocity between birth and 36-week corrected gestational age compared with intralipid Δ weight z-score (adjusted mean difference (aMD) −0.67; 95% CI −0.69, −0.39). Subgroup analysis indicated that mainly male infants in the SMOFlipid−LE group had a lower Δ weight z-score compared to those in the intralipid group (p < 0.001), with no difference observed in females (p = 0.82). SMOFlipid was associated with a lower rate of bronchopulmonary dysplasia (BPD) (aRR 0.61; 95% CI 0.46, 0.8) and higher rate of late-onset sepsis compared with intralipid (aRR 1.44; 95% CI 1.22−1.69). SMOFlipid was associated with lower growth velocity and BPD but higher rate of late-onset sepsis—it is a double-edged sword.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".