Risk factors for postoperative acute kidney injury in colorectal cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Purpose To thoroughly examine the risk factors that may predispose patients with colorectal cancer to postoperative acute kidney injury (AKI). Methods To find relevant studies (from the beginning up to May 2024), two researchers searched PubMed, Web of Science, the Cochrane Library, and Embase databases. Two researchers evaluated the quality of the literature using the Newcastle–Ottawa Scale (NOS) and extracted data individually. Data analysis was performed using the Review Manager 5.4. Results Our meta-analysis included 23 studies, encompassing a total of 167,904 patients. The identified risk factors for postoperative AKI in colorectal cancer patients were male sex, older age, body mass index (BMI) ≥ 25 kg/m2, hypertension, diabetes mellitus (DM), chronic kidney disease (CKD), hypoalbuminemia, emergency surgery, open surgery, prolonged operation time, American Society of Anesthesiologists (ASA) score ≥ 3, and intraoperative transfusion. In contrast, anemia and elevated creatinine levels did not emerge as significant risk factors for AKI in this population. Conclusion To mitigate the incidence of postoperative AKI among these patients, healthcare professionals must proactively identify these risk factors and implement appropriate preventive measures.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.025 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".