Risk factors for perioperative morbidity and mortality after extended hepatectomy for hepatocellular carcinoma
Bibliographic record
Abstract
BACKGROUND: Extended hepatectomy with resection of more than four segments is a high-risk operation, especially in patients with hepatocellular carcinoma (HCC) associated with chronic liver disease. This study evaluated the risk factors for morbidity and mortality following extended hepatectomy for HCC. METHODS: Preoperative and intraoperative variables of 155 patients who underwent extended hepatectomy for HCC were analysed to identify risk factors for postoperative morbidity and mortality. RESULTS: The overall morbidity rate was 55.5 per cent (n = 86). Most morbidity was due to ascites or pleural effusion. Significant life-threatening complications occurred in 20.0 per cent (n = 31). The perioperative mortality rate was 8.4 per cent (n = 13). Multivariate analysis found that portal clamping (P = 0.023) and perioperative blood transfusion (P < 0.001) were risk factors for morbidity, whereas perioperative blood transfusion (P < 0.001) was the only risk factor for significant morbidity. Co-morbid illness (P = 0.019) and perioperative blood transfusion (P = 0.004) were risk factors for perioperative mortality. CONCLUSION: Meticulous operative techniques to minimize blood loss and transfusion, while avoiding a prolonged Pringle manoeuvre, may help reduce postoperative morbidity. Avoidance of perioperative blood transfusion and careful preoperative selection of patients in terms of overall physiological status are important measures to reduce the postoperative mortality rate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".