Change in Estimated GFR and Risk of Allograft Failure in Patients Diagnosed With Late Active Antibody-mediated Rejection Following Kidney Transplantation
Bibliographic record
Abstract
BACKGROUND: There are challenges in designing adequate, well-controlled studies of patients with active antibody-mediated rejection (AMR) after kidney transplantation (KTx). METHODS: We assessed the functional relationship between change in estimated glomerular filtration rate (eGFR) following the diagnosis of AMR and the risk of subsequent death-censored graft failure using the joint modeling framework. We included recipients of solitary KTx between 1995 and 2013 at 4 transplant centers diagnosed with biopsy-proven active AMR at least 1 year post-KTx, who had a minimum of 3-year follow-up. RESULTS: A total of 91 patients across participating centers were included in the analysis. Of the 91 patients, n = 54 patients (59%) met the death-censored graft failure endpoint and n = 62 patients (68%) met the all-cause graft failure composite endpoint. Kaplan-Meier death-censored graft survival rates at 12, 36, and 60 months postdiagnosis of AMR pooled across centers were 88.9%, 58.9%, and 36.4%, respectively. Spaghetti plots indicated a linear trend in the change in eGFR, especially in the first 12 months postdiagnosis of active AMR. A significant change in eGFR was observed within the first 12 months postdiagnosis of active AMR, getting worse by a factor of -0.757 mL/min/1.73 m2 per month during the 12-month analysis period (a delta of -9.084 mL/min/1.73 m2 at 1 y). Notably, an extrapolated 30% improvement in the slope of eGFR in the first 12 months was associated with a 10% improvement in death-censored graft failure at 5 years. CONCLUSIONS: If prospectively validated, this study may inform the design of pivotal clinical trials for therapies for late AMR.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".