Stratification of Acute Kidney Injury Risk, Disease Severity, and Outcomes by Electrolyte Disturbances
Bibliographic record
Abstract
Acute kidney injury (AKI) affects up to 30% of all hospitalized patients in Central Europe and the USA. New biomarker molecules have been identified in recent years; most studies performed so far however aimed to identify markers for diagnostic purposes. Serum electrolytes such as sodium and potassium are quantified in more or less all hospitalized patients. Aim of the article is to review the literature on the AKI predictive role of four distinct serum electrolytes in evolving/progressing AKI. The following databases were searched for references: PubMed, Web of Science, Cochrane Library, and Scopus. The period lasted from 2010 until 2022. The following terms were utilized: "AKI" AND "sodium" OR "potassium" OR "calcium" OR "phosphate" AND "risk" OR "dialysis" OR "recovery of kidney function" OR "renal recovery" OR "kidney recovery" OR "outcome". Finally, 17 references were selected. The included studies were mostly retrospective in nature. Particularly, hyponatremia has been shown to be associated with an overall poor clinical outcome. The association between dysnatremia and AKI is anything but consistent. Hyperkalemia and potassium variability are most likely AKI predictive. Serum calcium and AKI risk are associated in a U-shaped manner. Higher phosphate levels potentially predict AKI in non-coronavirus disease 2019 (COVID-19) patients. The literature suggests that admission electrolytes can offer valuable information about AKI onset during follow-up. Limited data are however available on follow-up characteristics such as the need for dialysis or the chance of renal recovery. These aspects are of particular interest from the nephrologist's perspective.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".