RESOLVE: Recurrence Posttransplant Observational Study in Focal Segmental Glomerulosclerosis and Minimal Change Disease
Bibliographic record
Abstract
Introduction: The morbidity of recurrent focal segmental glomerulosclerosis (FSGS) and minimal change disease (MCD) after transplant is well recognized. Additional collaborative research is necessary to advance understanding of recurrence epidemiology, mechanisms, interventions, and outcomes, particularly in children. Methods: RESOLVE is a multicenter, observational cohort study examining the posttransplant course of patients with FSGS and MCD across the lifespan. Multiple enrollment options will facilitate both retrospective and prospective collection of biospecimens, self-report items, and electronic health record data across pediatric and adult participants. The study offers a unique mobile health option for participants to enroll and engage with the study remotely. Logistic regression using a log link function will evaluate recurrence risk within 3 months of transplant based on clinical characteristics and assess the impact of social determinants of health on time to graft failure, following adjustment. Cox proportional hazards models with primary outcome of graft failure with competing risk of death will evaluate the impact of recurrence therapy and access to preventative versus reactive recurrence therapy. Independent logistic regression will evaluate the impact of recurrence therapy and endophenotypes on proteinuric outcomes. Conclusion: Multiple enrollment approaches and tailored site participation are needed while studying recurrent FSGS (rFSGS) due to its rarity and phenotypic variability. RESOLVE provides a framework for international collaboration to unravel the course of rFSGS through a biospecimen and data repository. It also explores the potential for mobile health tools to enhance recruitment of participants and to promote cooperation among researchers to advance understanding of recurrence mechanisms and treatments.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".