Pediatric Outcomes in Transplant: PersOnaliSing Immunosuppression To ImproVe Efficacy (POSITIVE Study): The Collaboration and Design of a National Transplant Precision Medicine Program
Bibliographic record
Abstract
BACKGROUND: Despite age-related differences in biology, physiology, and behavior, transplant immunosuppression is not tailored by age. This likely contributes to high graft failure and posttransplant complications. We present the aims, design, and methods of the Pediatric Outcomes in Transplant: PersOnaliSing Immunosuppression To ImproVe Efficacy Study aimed at personalizing posttransplant immunosuppression in children and young adults. METHODS: In this prospective observational cohort study, we recruited pediatric and young adult solid organ transplant, pediatric allogeneic hematopoietic stem cell transplant recipients, and matched living and deceased organ donors from 14 transplant centers across Canada. Clinical data, questionnaires, biospecimens, and pharmacy records were collected at serial time points: (1) to identify genetic and host immune factors that influence immunosuppression dose requirements across different ages and transplant types, (2) to identify viral-host interactions that increase susceptibility to Epstein-Barr virus infection, and (3) to define care processes and structures associated with medication adherence in adolescents and young adults. RESULTS: From 2015 to 2018, 1662 new and prevalent transplant recipients were screened, 1166 were recruited for the various aims, including 370 liver, 445 kidney, 277 heart, 19 lung, 19 multiple, and 36 hematopoietic stem cell transplant transplants. Twelve percent were younger than 2 years, 30% were 2 to 10 years, 42% were 10 to 18 years, and 16% were 18 to 24 years at enrollment. Nine hundred thirty-one consented to participation in aims 1 and 2 (90% consent rate), 287 to aim 3 (82% consent rate). Biospecimens collected included 898 for DNA, 276 for immunoassays, and 717 for biomarker studies. Seventy percent participants have completed follow-up; 30% are pending study completion. CONCLUSIONS: The design of this national multicenter cross-organ network helped maximize recruitment of a large patient cohort for studying age and organ-related differences in immunosuppression needs that would not otherwise be feasible. Leveraging the unique clinical, biological, environmental, and behavioral characteristics of this cohort will help develop precision medicine strategies for individualizing posttransplant immunosuppression.
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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 | 0.022 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".