Race Affects Outcome Among Infants With Intestinal Failure
Bibliographic record
Abstract
OBJECTIVE: Intestinal failure (IF) is a rare, devastating condition associated with significant morbidity and mortality. We sought to determine whether ethnic and racial differences were associated with patient survival and likelihood of receiving an intestinal transplant in a contemporary cohort of children with IF. METHODS: This was an analysis of a multicenter cohort study with data collected from chart review conducted by the Pediatric Intestinal Failure Consortium. Entry criteria included infants ≤ 12 months receiving parenteral nutrition (PN) for ≥ 60 continuous days and studied for at least 2 years. Outcomes included death and intestinal transplantation (ITx). Race and ethnicity were recorded as they were in the medical record. For purposes of statistical comparisons and regression modeling, categories of race were consolidated into "white" and "nonwhite" children. RESULTS: Of 272 subjects enrolled, 204 white and 46 nonwhite children were available for analysis. The 48-month cumulative incidence probability of death without ITx was 0.40 for nonwhite and 0.16 for white children (P < 0.001); the cumulative incidence probability of ITx was 0.07 for nonwhite versus 0.31 for white children (P = 0.003). The associations between race and outcomes remained after accounting for low birth weight, diagnosis, and being seen at a transplant center. CONCLUSIONS: Race is associated with death and receiving an ITx in a large cohort of children with IF. This study highlights the need to investigate reasons for this apparent racial disparity in outcome among children with IF.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".