Modeling Outcomes in Children With Biliary Atresia With Native Liver After 2 Years of Age
Bibliographic record
Abstract
Approximately 50% of infants with biliary atresia (BA) undergoing Kasai portoenterostomy show survival with native liver (SNL) at age 2 years. Predictors of disease progression after age 2 years are unknown, despite estimates of 20%‐30% undergoing liver transplant (LT) between age 2 and 18 years. We sought to address this knowledge gap by developing prognostic models in participants of the multicenter prospective National Institutes of Health‐supported Childhood Liver Disease Research Network. We extracted 14 clinical and biochemical variables at age 2 years to develop two models for future outcomes: 1) LT or death (LTD) and 2) first sentinel event (SE), either new onset ascites, hepatopulmonary syndrome (HPS), or gastrointestinal (GI) bleed. A total of 240 participants, enrolled between 2004 and 2017, were followed until a median age of 5.1 years (range, 2.0‐13.3 years). Of these participants, 38 underwent LT (n = 37) or death (n = 1); cumulative incidence, 23.7% (95% confidence interval [CI], 16.2%‐32.0%). Twenty‐seven experienced either new‐onset ascites (n = 13), HPS (n = 1), or GI bleed (n = 14). One participant had ascites and GI bleed concurrently; cumulative incidence, 21.5% (95% CI, 14.2%‐29.8%) by age 10 years. The Cox proportional hazard model predicted risk of LTD, using total bilirubin, albumin, platelet count, and history of either ascites or cholangitis (BA LTD model), with a C‐index of 0.88 (range, 0.86‐0.89). A cause‐specific hazard competing risk model predicted SE using platelet count and gamma glutamyltransferase levels (BA SE model) with a C‐index of 0.81 (range, 0.80‐0.84). Internal model validity was assessed using Harrell’s C‐index with cross‐validation. Conclusion: Stratification using these models identified risk of poor outcomes in patients with BA SNL after age 2 years. The models may identify those who would benefit from enhanced clinical surveillance and prioritization in clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".