Share Seamlessly, Steal Shamelessly: Unlocking the Learning Health Network Ethos in Pediatric Liver Transplantation
Bibliographic record
Abstract
BACKGROUND: Pediatric liver transplant is a life-saving procedure, but risks remain for children in the pre-, peri-, and post-transplant period-impacting survival, graft, and child health, and quality of life. Learning health networks offer a structure that multi-disciplinary teams across many centers can use to collaborate on improving outcomes for children with rare diseases-with a mantra of "sharing seamlessly" to coordinate across centers and "stealing shamelessly" from best practices within or outside of the network. METHODS: We describe the development of the Starzl Network for Excellence in Pediatric Transplantation (SNEPT) as a learning health network dedicated to improving health and quality of life for children with liver transplants and their families. RESULTS: SNEPT was founded in 2017 as a collaborative effort between pediatric liver transplant centers and the families that they serve, initially at 10 and then 16 centers across the US and Canada. Stakeholders identified four priority projects: Peri-Operative and Surgical Practices, Optimizing Immunosuppression, Quality of Life, and Transition of Care. Over the last 8 years, we built SNEPT infrastructure to efficiently support work on each project. Our four working groups each developed methods to improve practice by sharing protocols, data, and experience between centers and adapting best-practice strategies from other centers for efficient improvement. CONCLUSIONS: Coordination as a learning health network has improved transparency and enhanced collaboration between SNEPT centers, creating opportunities for best-practices development and better outcomes in pediatric liver transplant.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".