Recent trends in postoperative mortality after liver resection- A systemic review and metanalysis of studies published in last 5 years and metaregression of various factors affecting 90 days mortality
Bibliographic record
Abstract
Abstract Aim The aim of this systemic review and meta-analysis was to analyse 90 days mortality after liver resection, and also study various factors associated with mortality via univariate and multivariate metaregression. Methods PubMed, Cochrane library, Embase, google scholar, web of science with keywords like ‘liver resection”; “mortality”;” hepatectomy”. Weighted percentage 90 days mortalities were analysed. univariate metaregression was done by DerSimonian-Liard methods. Major hepatectomy, open surgery, cirrhotic livers, blood loss, hepatectomy for hepatocellular carcinoma, hepatectomy for colorectal liver metastasis were taken as moderators in metaregression analysis. We decided to enter all co-variants in multivariate model to look for mixed effects. Heterogeneity was assessed using the Higgins I 2 test, with values of 25%, 50% and 75% indicating low, moderate and high degrees of heterogeneity. Cohort studies were assessed for bias using the Newcastle-Ottawa Scale to assess for the risk of bias. Publication bias was assessed using funnel plot. Funnel plot asymmetry was evaluated by Egger’s test. Results Total 29931 patients’ data who underwent liver resections for various etiologies were pooled from 41 studied included1257 patients died within 90 days post operatively. Weighted 90 days mortality was 3.6% (95% C.I 2.8% −4.4%). However, heterogeneity of the analysis was high with I 2 94.625%.(p<0.001). We analysed various covariates like major hepatectomy, Age of the patient, blood loss, open surgery, liver resections done for hepatocellular carcinoma or colorectal liver metastasis and cirrhotic liver to check for their association with heterogeneity in the analysis and hence 90 days mortality. On univariate metaregression analysis major hepatectomy (p<0.001), Open hepatectomy (p<0.001), blood loss (p=0.002) was associated with heterogeneity in the analysis and 90 days mortality. On multivariate metaregression Major hepatectomy(p=0.003) and Open surgery (p=0.012) was independently associated with higher 90 days mortality, and liver resection for colorectal liver metastasis was independently associated with lesser 90 days mortality (z= −4.11,p<0.01). Residual heterogeneity after all factor multivariate metaregression model was none (I 2 =0,Tau 2 =0, H 2 =1) and nonsignificant (p=0.49). Conclusion Major hepatectomy, open hepatectomy, and cirrhotic background is associated with higher mortality rates and colorectal liver metastasis is associated with lower peri operative mortality rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.045 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".