Morbidity, mortality and predictors of outcome following hepatectomy at a Saudi tertiary care center
Bibliographic record
Abstract
BACKGROUND: Hepatic resection is a major surgical procedure. Data on outcomes of hepatectomy in Saudi Arabia are scarce. OBJECTIVE: To measure morbidity and mortality and assess predictors of outcome after hepatectomy. DESIGN: Descriptive study. SETTING: Tertiary care center in Saudi Arabia with well established hepatobiliary surgery unit. PATIENTS AND METHODS: All patients undergoing liver resection in our institute during 2006-2014. Data were analyzed by Kaplan-Meier survival analysis. MAIN OUTCOMES MEASURE(S): Postoperative morbidity and 90-day mortality. Secondary outcomes were risk factors associated with increased morbidity and mortality. RESULTS: Data on 77 resections were collected; 56 patients (72.7%) had a malignant etiology, mainly colorectal liver metastases and hepatocellular carcinoma (45.5% and 14.3% respectively). Complications developed following 30 resections (39.0%), with the majority being Clavien grades I-III. In the univariate analysis, predicting factors were the total bilirubin level preoperatively, operative time, extent of resection (i.e., major resection), use of epidural anesthesia, and postoperative liver dysfunction. In the multivariate analysis, the Schindl liver dysfunction score showed the strongest correlation with the development of complications (P=.006). The 90-day postoperative mortality was 5.2% (4/77 patients); 3 patients fulfilled the 50:50 liver dysfunction criteria. Significant predictors were concurrent intra-abdominal surgery, postoperative liver dysfunction, and multiple complications. CONCLUSION: Factors that predicted development of complications were elevated total bilirubin level preoperatively, operative time, extent of the resection, use of epidural anesthesia and a postoperative need for blood transfusion. Liver resection is a safe and feasible option at our center. LIMITATIONS: The small number of indications for resection and consequent reduction in variety of risk factors limited ability to make inferences. Additionally, only a handful of cases were performed laparoscopically.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".