Reducing Pediatric Liver Transplant Complications: A Potential Roadmap for Transplant Quality Improvement Initiatives Within North America
Bibliographic record
Abstract
Though robust clinical data are available within transplantation, these data are not used for broad-based, multicentered quality improvement initiates. This article describes a targeted quality improvement initiative within the Studies of Pediatric Liver Transplantation (SPLIT) Registry. Using standard statistical techniques and clinical expertise to adjust for data and statistical reliability, we identified the pediatric liver transplant centers in North America with the lowest hepatic artery thrombosis rate and biliary complication rates. A survey was completed to establish current practices within the entire SPLIT group. Surgeons from the highest performing centers presented a detailed, technically oriented overview of their current practices. The presentations and discussion that followed were recorded and form the basis of the best practices described herein. We frame this work as a unique six-step approach roadmap that may serve as an efficient and cost effective model for novel broad-based quality improvement initiatives within 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 | 0.042 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".