Incidence, risk factors and outcomes of postoperative acute kidney injury in elderly patients undergoing abdominal surgery
Bibliographic record
Abstract
Abstract Objectives To investigate the incidence, risk factors and outcomes of acute kidney injury (AKI) in elderly patients undergoing abdominal surgery. Methods A retrospective study exploring the incidence of AKI in patients older than 75 years within 48 hours after abdominal surgery was conducted. Patients' preoperative characteristics, intraoperative management including medication and outcomes were evaluated for associations with AKI using a logistic regression model.Results During the 2.5-year period, a total of 409 abdominal surgeries were performed. Both pre- and post-operative SCr measurements were available for 329 (80.4%) cases. 26 patients (7.9%) developed AKI, of whom 25 (7.6%) and 1 (0.3%) reached the AKI stages 1 and 2 respectively. Older age (83.0 vs 80.4 years; p=0.002), preoperative liver function damage represented by AST (47.5 vs 21.0 IU/L; p=0.023), intraoperative combined administration of hydroxyethyl starch(HES) and furosemide (15.38% vs 1.65%; p=0.003) were independent risk factors for the development of postoperative AKI. Furthermore, AKI patients had significantly longer ICU stay (3 vs 0 days; p<0.001) and higher in-hospital mortality (23.08% vs 2.31%; p<0.001)Conclusion Intraoperative combined administration of HES and furosemide is an independent factor which can be controlled by anesthesiologists and surgeons for AKI. This provides important recommendations for reducing the incidence of postoperative AKI.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".