Bibliographic record
Abstract
The landscape of kidney disease in diabetes has shifted. The classical dogma of "diabetic nephropathy" progressing through stages of albuminuria, leading to decline in glomerular filtration rate and end-stage kidney disease (ESKD), has been replaced by a more nuanced understanding of the complex and heterogeneous nature of kidney disease in diabetes. Paralleling this evolution, standardized definitions have resulted in a growing appreciation that acute kidney injury (AKI) is increasing in its incidence rapidly and that people with diabetes are much more likely to develop AKI than people without diabetes. Here, I propose that AKI should be considered a complication of diabetes alongside other complications that similarly do not fit neatly into the historical microvascular/macrovascular paradigm. In this article, we take a look at the evidence indicating that diabetes is a major risk factor for AKI and we review the causes of this increased risk. We consider the long-term implications of AKI in diabetes and its potential contribution to the future development of chronic kidney disease, ESKD, and mortality. Finally, we look toward the future at strategies to better identify people at risk for AKI and to develop new approaches to improve AKI outcomes. Recognizing AKI as a bona fide complication of diabetes should open up new avenues for investigation that may ultimately improve the outlook for people living with diabetes and at risk for kidney 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".