Association between SMOFlipid and impaired brain development on term-equivalent age brain magnetic resonance imaging in very preterm infants
Bibliographic record
Abstract
Soybean oil, medium-chain triglycerides, olive oil, and fish oil (SMOFlipid) is used without evidence of benefits. We investigated the relationship between lipid emulsions and brain injury in term-equivalent age magnetic resonance imaging (MRI) in 148 very preterm infants with a birth weight of < 1500 g at ≤ 32 gestational weeks in a neonatal intensive care unit. Infants who received soybean-based lipid emulsions between January 2015 and December 2018 were compared with those who received SMOFlipids between January 2019 and December 2022. A negative binomial generalized linear model was applied for bivariate analysis. Modified log-Poisson regression with generalized linear models and a robust variance estimator (Huber-White) were applied to adjust for potential confounders. The Kidokoro score was used to determine if lipid emulsion type would affect brain morphology and growth at term-equivalent age. Eighty-six (58.9%) received SMOFlipid. SMOFlipid was associated with lower focal signal abnormality, myelination delay, increased extracerebral space, and cerebellar volume reduction (P = 0.02, P = 0.007, P = 0.01, P = 0.02, respectively). SMOFlipidis are associated with brain insult, especially in white matter, cortical gray matter, and the cerebellum. Well-designed studies are needed to investigate the effect of lipid emulsions on the central nervous system.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".