A Learning Health System for Pediatric Liver Transplant
Bibliographic record
Abstract
OBJECTIVE: Learning health systems (LHS) integrate research, improvement, management, and patient care, such that every child receives "the right care at the right time...every time," that is, evidence-based, personalized medicine. Here, we report our efforts to establish a sustainable, productive, multicenter LHS focused on pediatric liver transplantation. METHODS: The Starzl Network for Excellence in Pediatric Transplantation (SNEPT) is the first multicenter effort by pediatric liver transplant families and providers to develop shared priorities and a shared agenda for innovation in clinical care. This report outlines SNEPT's structure, accomplishments, and challenges as an LHS. RESULTS: We prioritized 4 initial projects: immunosuppression, perioperative anticoagulation, quality of life, and transition of care. We shared center protocols/management to identify areas of practice variability between centers. We prioritized actionable items that address barriers to providing "the right care at the right time" to every pediatric liver transplant recipient: facilitating transparency of practice variation and the connection of practices to patient outcomes, harnessing existing datasets to reduce the burden of tracking outcomes, incorporating patient-reported outcomes into outcome metrics, and accelerating the implementation of knowledge into clinical practice. This has allowed us to strengthen collaborative relationships, design quality improvement projects, and collect pilot data for each of our priority projects. CONCLUSIONS: The field of pediatric liver transplantation can be advanced through application of LHS principles. Going forward, SNEPT will continue to unite patient advocacy, big data, technology, and transplant thought leaders to deliver the best care, while developing new, scalable solutions to pediatric transplantation's most challenging problems.
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 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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".