Risk factors for bile leakage after hepatectomy without biliary reconstruction: A Meta-analysis
Bibliographic record
Abstract
ObjectiveTo investigate the risk factors for bile leakage after hepatectomy without biliary reconstruction . MethodsCNKI, Wanfang Data, VIP, PubMed, Embase, Web of Science, and The Cochrane Library were searched for English and Chinese study reports on the risk factors for bile leakage after hepatectomy without biliary reconstruction published up to April 2021. The method of Cochrane systematic review was used for literature screening and data extraction, and Newcastle-Ottawa Scale was used for quality assessment. RevMan 54 software was used to perform a meta-analysis of the extracted data. ResultsA total of 16 articles (13 in English and 3 in Chinese) were included in this study, with a total of 16036 cases. The meta-analysis showed that sex (odds ratio ［OR］=1.27, 95%CI: 1.09-1.48, P=0.003), diabetes (OR=1.23, 95%CI: 1.07-1.41, P=0.003), past history of liver surgery (OR=2.50, 95%CI: 1.74-3.59, P＜0.001), anatomic hepatectomy (OR=1.58, 95%CI: 1.09-2.30, P=0.02), segment I hepatectomy (OR=2.56, 95%CI: 1.50-4.40, P＜0.001), central hepatectomy (S4, S5, S8) (OR=3.51, 95%CI: 2.80-4.40, P＜0.001), left third hepatectomy (OR=353, 95%CI: 2.32-5.36, P＜0.001), and intraoperative blood transfusion (OR=2.64, 95%CI: 1.93-3.60, P＜0.001) were the risk factors for bile leakage after hepatectomy. Liver cirrhosis, preoperative liver function grade, preoperative chemotherapy, and left/right hemihepatectomy were not the risk factors for bile leakage. ConclusionThere are complex influencing factors for bile leakage after hepatectomy, and in addition to the patient’s own factors such as sex, diabetes, and past history of liver surgery, intraoperative factors, such as surgical procedures, extent of hepatectomy, and intraoperative blood transfusion, are also risk factors for bile leakage after hepatectomy. The surgeon should conduct adequate preoperative assessment and perform careful operation during surgery to reduce the incidence rate of postoperative bile leakage.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.057 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".