Risk factors associated with allograft failure in pediatric kidney transplant recipients with focal segmental glomerulosclerosis
Bibliographic record
Abstract
BACKGROUND: With improved outcomes for children transplanted with FSGS since previous NAPRTCS registry reports, this study re-evaluates the association of living donation, immunosuppression, and DGF on graft survival. SETTING: Patients transplanted between 2002 and 2016, comparing FSGS diagnosis vs other glomerular diseases. METHODS: Primary outcomes were allograft survival and FSGS recurrent-free graft survival. Potential risk factors were obtained at the time of transplant and up to 30 days post-transplantation. Analysis considered a priori that DGF may be a proxy for severe FSGS recurrence. Multivariable survival models for outcome were tested for sensitivity without/with DGF to determine features independent of recurrence. RESULTS: From the larger cohort of 3010 patients, 5-year graft survival in children with FSGS (n = 455) was worse (74.3%) compared with other glomerular diseases (87.1%, n = 690) (HR 1.45, P = 0.033). Modeling all glomerular diseases, survival risk was associated with deceased donor (HR 1.83, P = 0.002), re-transplantation (HR 1.58, P = 0.013), and recipient age (HR 1.06/y, P = 0.002). The living donor advantage was not confirmed in a FSGS model (HR 1.51 for deceased, P = 0.12). DGF was highly associated with graft failure (HR 4.39, P < 0.001) and independent of re-transplant history but not FSGS diagnosis. Induction agents or primary immunosuppression choices were not associated with survival. CONCLUSION: Graft survival rates have improved since the previous report. Living donor did not predict graft failure, but there remains no survival advantage. DGF was the primary independent predictor for graft loss secondary to FSGS recurrence, consistent with DGF being a proxy for severe recurrent disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".