Major comorbid disease processes associated with increased incidence of acute kidney injury
Bibliographic record
Abstract
Acute kidney injury (AKI) is commonly seen amongst critically ill and hospitalized patients. Individuals with certain co-morbid diseases have an increased risk of developing AKI. Thus, recognizing the co-morbidities that predispose patients to AKI is important in AKI prevention and treatment. Some of the most common co-morbid disease processes that increase the risk of AKI are diabetes, cancer, cardiac surgery and human immunodeficiency virus (HIV) acquired immune deficiency syndrome (AIDS). This review article identifies the increased risk of acquiring AKI with given co-morbid diseases. Furthermore, the pathophysiological mechanisms underlying AKI in relation to co-morbid diseases are discussed to understand how the risk of acquiring AKI is increased. This paper reviews the effects of various co-morbid diseases including: Diabetes, cancer, cardiovascular disease and HIV AIDS, which all exhibit a significant increased risk of developing AKI. Amongst these co-morbid diseases, inflammation, the use of nephrotoxic agents, and hypoperfusion to the kidneys have been shown to be major pathological processes that predisposes individuals to AKI. The pathogenesis of kidney injury is complex, however, effective treatment of the co-morbid disease processes may reduce its risk. Therefore, improved management of co-morbid diseases may prevent some of the underlying pathology that contributes to the increased risk of developing AKI.
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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 | 0.000 | 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 | 0.000 |
| 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".