The Impact of DGF On Kidney Allograft Survival: A Paired Kidney Analysis.
Bibliographic record
Abstract
The long-term implications of DGF remain uncertain. To remove the confounding effect of donor factors, we examined the impact of DGF on post-transplant outcomes in a paired kidney analysis, where one recipient had DGF and the recipient of the mate kidney from the same donor did not. We identified n=100,248 deceased donors between 1998-2011 in SRTR where kidneys were transplanted to separate recipients. In n=10,256(10%) of donors, both recipients developed DGF; in n=30,302(30%) donors 1 recipient developed DGF (discordant DGF; and in n=59,690(60%) donors, neither recipient developed DGF. In transplant pairs with discordant DGF, we used conditional Cox regression to determine the association of DGF with death censored graft loss and all cause graft loss and further examined the association of DGF with all cause graft loss in cases of DGF with and without concomitant acute rejection at the time of discharge after transplantation. Results: After adjustment for differences in relevant transplant and recipient factors, DGF was associated with a 1.8 fold increased risk of all cause graft loss and a 2.2 fold increase in the risk of death censored graft loss. DGF with concurrent acute rejection was associated with a 3 fold increase in all cause graft loss, whereas DGF without concurrent acute rejection was associated with a 1.7 fold increase in all cause graft loss(Table). The following factors were associated with DGF: male sex, black race, a history of diabetes or peripheral vascular disease, prior transplantation, PRA≥20, HLA mismatch, cold ischemic time>12 hours, and the use of cold storage. Conclusion: These results suggest that the occurrence of DGF confers an independent risk of graft loss in kidney transplant recipients.Table: No Caption available.
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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 | 0.000 |
| 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".