Risk, Predictors, and Outcomes of Acute Kidney Injury in Patients Admitted to Intensive Care Units in Egypt
Bibliographic record
Abstract
Epidemiology of acute kidney injury (AKI) in developing countries is under-studied. We evaluated the risk and prognosis of AKI in patients admitted to intensive care units (ICUs) in Egypt. We recruited consecutive adults admitted to ICUs in Alexandria Teaching Hospitals over six months. We used the KDIGO criteria for AKI. We followed participants until the earliest of ICU discharge, death, day 30 from entry or study end. Of the 532 participants (median age 45 (Interquartile range [IQR]: 30-62) years, 41.7% male, 23.7% diabetics), 39.6% had AKI at ICU admission and 37.4% developed AKI after 24 hours of ICU admission. Previous need of diuretics, sepsis and low education were associated with AKI at ICU admission; APACHE II score independently predicted AKI after ICU admission. A total of 120 (22.6%) patients died during 30-day follow-up. Compared to patients who remained AKI-free, mortality was significantly higher in patients who had AKI at study entry (Hazard Ratio [HR] 2.14; 95% Confidence Interval [CI] 1.02-4.48) or developed AKI in ICU (HR 2.74; 95% CI 1.45-5.17). The risk of AKI is high in critically ill people and predicts poor outcomes. Further studies are needed to estimate the burden of AKI among patients before ICU admission.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".