Long-Term Follow-Up of Kidney Function after Acute Liver Failure or Acute Liver Injury: A Cohort Study
Bibliographic record
Abstract
Introduction: Acute liver failure (ALF) is a rare disease with high mortality. Acute kidney injury (AKI) following ALF is frequent. We assessed AKI impact on long-term kidney function among ALF survivors. Methods: Observational cohort study including consecutive adult (age ≥16 years) patients with ALF or acute liver injury (ALI) admitted to a Portuguese tertiary center intensive care unit (ICU) between October 2013 and February 2020. KDIGO criteria were used to define AKI and chronic kidney disease (CKD). Primary outcome was the estimated glomerular filtration rate (eGFR), defined by the Chronic Kidney Disease Epidemiology Collaboration formula, at least 1 year after index ICU admission. Results: Among 104 patients with ALF (n = 74) or ALI (n = 30), mean (SD) age was 43.7 (18.0) years, and 44 were male. Among all patients (n = 104), following adjustment for age and SOFA score, AKI during the first 7 ICU days (n AKI = 57 and n renal replacement therapy [RRT] = 32) was independently associated with all-cause mortality (adjusted HR [95% CI] 11.61 [1.49–90.34]; p = 0.019). Among hospital survivors with long-term kidney function available (n = 56), median (interquartile range) >1 year eGFR was 95.3 (75.0–107.7) mL/min/1.73 m2 (mean [SD] follow-up of 3.1 [1.6] years). Among these hospital survivors, following adjustment for baseline eGFR, AKI during the first 7 ICU days (n AKI = 19 and n RRT = 10) was not associated with >1 year eGFR (p = 0.15). At least 1 year after index ICU admission, 5 patients developed CKD, none RRT-dependent. Conclusions: Among ALF or ALI survivors, AKI was not associated with significant long-term loss of kidney function.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".