Predicting ideal outcome after pediatric liver transplantation: An exploratory study using machine learning analyses to leverage Studies of Pediatric Liver Transplantation Data
Bibliographic record
Abstract
Machine learning analyses allow for the consideration of numerous variables in order to accommodate complex relationships that would not otherwise be apparent in traditional statistical methods to better classify patient risk. The SPLIT registry data were analyzed to determine whether baseline demographic factors and clinical/biochemical factors in the first-year post-transplant could predict ideal outcome at 3 years (IO-3) after LT. Participants who received their first, isolated LT between 2002 and 2006 and had follow-up data 3 years post-LT were included. IO-3 was defined as alive at 3 years, normal ALT (<50) or GGT (<50), normal GFR, no non-liver transplants, no cytopenias, and no PTLD. Heat map analysis and RFA were used to characterize the impact of baseline and 1-year factors on IO-3. 887/1482 SPLIT participants met inclusion criteria; 334 had IO-3. Demographic, biochemical, and clinical variables did not elucidate a visual signal on heat map analysis. RFA identified non-white race (vs white race), increased length of operation, vascular and biliary complications within 30 days, and duct-to-duct biliary anastomosis to be negatively associated with IO-3. UNOS regions 2 and 5 were also identified as important factors. RFA had an accuracy rate of 0.71 (95% CI: 0.68-0.74), PPV = 0.83, and NPV = 0.70. RFA identified participant variables that predicted IO-3. These findings may allow for better risk stratification and personalization of care following pediatric liver transplantation.
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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 | 0.007 | 0.015 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".