Marked variation in the definition and diagnosis of delayed graft function: a systematic review
Bibliographic record
Abstract
BACKGROUND: The term delayed graft function (DGF) is commonly used to describe the need for dialysis after receiving a kidney transplant. DGF increases morbidity after transplantation, prolongs hospitalization and may lead to premature graft failure. Various definitions of DGF are used in the literature without a uniformly accepted technique to identify DGF. METHODS: We performed a systematic review of the literature to identify all of the different definitions and diagnostic techniques to identify DGF. RESULTS: We identified 18 unique definitions for DGF and 10 diagnostic techniques to identify DGF. CONCLUSIONS: The utilization of heterogeneous clinical criteria to define DGF has certain limitations. It will lead to delayed and sometimes inaccurate diagnosis of DGF. Hence a diagnostic test that identifies DGF reliably and early is necessary. Heterogeneity, in the definitions used for DGF, hinders the evolution of a diagnostic technique to identify DGF, which requires a gold standard definition. We are in need of a new definition that is uniformly accepted across the kidney transplant community. The new definition will be helpful in promoting better communication among transplant professionals and aids in comparing clinical studies of diagnostic techniques to identify DGF and thus may facilitate clinical trials of interventions for the treatment of 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 | 0.020 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".