Acute kidney injury following resection of hepatocellular carcinoma: prognostic value of the acute kidney injury network criteria
Bibliographic record
Abstract
Background: Acute kidney injury (AKI) is associated with increased morbidity and mortality after liver resection. Patients with hepatocellular carcinoma (HCC) have a higher risk of AKI owing to the underlying association between hepatic and renal dysfunction. Use of the Acute Kidney Injury Network (AKIN) diagnostic criteria is recommended for patients with cirrhosis, but remains poorly studied following liver resection. We compared the prognostic value of the AKIN creatinine and urine output criteria in terms of postoperative outcomes following liver resection for HCC. Methods: All patients who underwent a liver resection for HCC from January 2010 to June 2016 were included. We used AKIN urine output and creatinine criteria to assess for AKI within 48 hours of surgery. Results: Eighty liver resections were performed during the study period. Cirrhosis was confirmed in 80%. Median hospital stay was 9 (interquartile range 7–12) days, and 30-day mortality was 2.5%. The incidence of AKI was higher based on the urine output than on the creatinine criterion (53.8% v. 20%), and was associated with prolonged hospitalization and 30-day postoperative mortality when defined by serum creatinine (hospital stay: 11.2 v. 20.1 d, p = 0.01; mortality: 12.5% v. 0%, p < 0.01), but not urine output (hospital stay: 15.6 v. 10 d, p = 0.05; mortality: 2.3% v. 2.7%, p > 0.99). Conclusion The urine output criterion resulted in an overestimation of AKI and compromised the prognostic value of AKIN criteria. Revision may be required to account for the exacerbated physiologic postoperative reduction in urine output in patients with HCC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 | 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".