Acute kidney injury in diabetic patients: A narrative review
Bibliographic record
Abstract
Diabetes mellitus (DM) is the most common cause of chronic kidney disease, which leads to end-stage renal failure worldwide. Glomerular damage, renal arteriosclerosis, and atherosclerosis are the contributing factors in diabetic patients, leading to the progression of kidney damage. Diabetes is a distinct risk factor for acute kidney injury (AKI) and AKI is associated with faster advancement of renal disease in patients with diabetes. The long-term consequences of AKI include the development of end-stage renal disease, higher cardiovascular and cerebral events, poor quality of life, and high morbidity and mortality. In general, not many studies discussed extensively "AKI in DM." Moreover, articles addressing this topic are scarce. It is also important to know the cause of AKI in diabetic patients so that timely intervention and preventive strategies can be implemented to decrease kidney injury. Aim of this review article is to address the epidemiology of AKI, its risk factors, different pathophysiological mechanisms, how AKI differs between diabetic and nondiabetic patients and its preventive and therapeutic implications in diabetics. The increasing occurrence and prevalence of AKI and DM, as well as other pertinent issues, motivated us to address this topic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".