Multicenter data to improve health for pediatric renal transplant recipients in North America: Complementary approaches of NAPRTCS and IROC
Bibliographic record
Abstract
Kidney transplantation increases life expectancy and improves quality of life for children with end-stage kidney disease, yet sequelae of transplantation and treatment make it difficult for transplant recipients to enjoy health and quality of life similar to their healthy peers. The NAPRTCS network was among the first to use multicenter data to inform improvements in care and outcomes for children with a kidney transplant through observational research. Now, with new technologies and unprecedented access to data, it is possible to create learning health systems as envisioned by the US National Academy of Sciences to seamlessly integrate research and continuous improvement of clinical care. In this review, we present two pre-eminent North American networks focused on using multicenter data to drive improved care and outcomes for children with a kidney transplant. Whereas, for the past 30 years NAPRTCS has focused on discovery of best practices through observational research and clinical trials, the Improving Renal Outcomes Collaborative, established in 2016, engages patients, families, clinicians, and researchers in redesigning the healthcare delivery system to enable practice change and continuous improvement of health outcomes. We discuss the history and past contributions of these networks, as well as current activities, barriers, and potential future solutions to more fully realize the vision of a true learning health system for pediatric kidney transplant recipients.
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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 | 0.091 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".