Current opinions on DGF management practices: A survey of the United States and Canada
Bibliographic record
Abstract
INTRODUCTION: Significant center-to-center variation in attitudes and management of delayed graft function (DGF) remains common. METHODS: A survey to describe current DGF practices was developed by workgroup members sponsored by the National Kidney Foundation (NKF) and was distributed to both the NKF DGF workgroup members, kidney transplant program directors and the transplant community within the United States and Canada. Seventy-one percent of NKF workgroup members completed the survey along with 70 unique the United States and three Canadian kidney transplant programs. All Organ Procurement and Transplantation Network (OPTN) regions were represented. RESULTS: DGF was reported to occur at rate of 20%-40% for most centers with 3.9% indicating their incidence to be >60%. Most centers reported longer hospital lengths of stay and more frequent outpatient visits. Despite the commonality of DGF, only half of centers reported having an established protocol to manage DGF. Kidney allograft biopsies were the only consistent DGF management strategy observed, although use of machine perfusion was also heavily favored. Other DGF management strategies voiced by a minority included having established outpatient practices to care for DGF patients and administering outpatient community-based hemodialysis. CONCLUSION: Although approximately a third of survey responders indicated that risk of DGF played a role in their willingness to accept organs, most did not feel that increased cost or clinical impact on outcomes was a deterrent. Future strategies, including broader sharing of best practices, redefining terminology specific to DGF, the establishment of DGF dialysis guidelines and improving access to machine perfusion across OPOs may help reduce discard and improve utilization of kidneys at risk for DGF.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".