The association of human milk intake and outcomes in biliary atresia
Bibliographic record
Abstract
OBJECTIVES: Human milk intake has many benefits which could influence outcomes in biliary atresia (BA). However, the role of human milk in BA has not been examined. We hypothesized that human milk intake would be associated with improved outcomes in BA. METHODS: We assessed the impact of any human milk (AHM) as compared to formula only (FO) intake before Kasai portoenterostomy (KP) on outcomes in 447 infants with BA using the PROBE database (NCT00061828) post hoc. The primary outcome was clearance of jaundice (COJ = total bilirubin (TB) < 2 mg/dL by 3 months post-KP). Secondary outcomes included 2-year survival with native liver (SNL), bilirubin levels, cholangitis, ascites, and growth. We assessed the fecal microbiome (n = 8) comparing AHM versus FO. RESULTS: At baseline, 211 infants received AHM and 215 received FO. 53.9% of AHM and 50.5% of FO achieved COJ (p = NS). SNL was insignificantly increased in AHM (odds ratio = 1.47, 95% confidence interval: 1.00-2.12, p = 0.053). TB decreased in AHM from 4 weeks to 3 months post-KP [4.8-4.0 mg/dL (p = 0.01)] unlike the FO group (4.9-4.9 mg/dL, p = 0.4). At 3 months post-KP, AHM infants had greater weight gain (1.88 ± 0.66 vs. 1.57 ± 0.73 kg, p < 0.001) and mid-upper arm circumference (12.9 ± 1.4 vs. 12.2 ± 1.7 cm, p < 0.001). Other secondary outcomes were not different. Microbiome differences were seen between AHM and FO. CONCLUSIONS: Human milk intake in infants with BA did not significantly improve COJ or SNL. However, growth parameters were improved, and TB 3 months post-KP was decreased. Thus, human milk intake should not be discouraged. Prospective studies with detailed assessment of human milk intake are needed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".