Acute Kidney Injury in Autoimmune-Mediated Rheumatic Diseases
Bibliographic record
Abstract
Acute kidney injury (AKI) is increasingly affecting hospitalized patients worldwide. Patients with inflammatory rheumatic diseases, although primarily impacted by functional impairment and sometimes structural damage to joints, bones, and muscle tissue, may also develop AKI during the course of their disease. This narrative review aimed to summarize potential causes of AKI and the associated disease patterns. The following databases were searched for references: PubMed, Web of Science, Cochrane Library, and Scopus. The search period covered from 1958 to 2024. Certain inflammatory rheumatic diseases increase the risk of AKI due to specific types of kidney disease. However, the most common conditions, such as rheumatoid arthritis and spondylarthritis, rarely cause AKI directly. Among the medications used for pain and sometimes disease activity control, nonsteroidal anti-inflammatory drugs (NSAIDs) can potentially induce AKI, even progressing to acute tubular necrosis. There is evidence that certain rheumatic diseases are associated with increased risk of AKI, independently of directly affecting kidney function or structure. However, the data on this topic are quite limited. AKI is a potentially significant issue for patients with inflammatory rheumatic diseases. Additional data on the increased risk of AKI, independent of direct kidney involvement, are needed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| 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 | 0.000 |
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".