Colorectal Surgery in Patients with Liver Cirrhosis: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Colorectal surgery in patients with liver cirrhosis poses a significant challenge due to the associated peri-operative morbidity and mortality risks. The aim of this systematic review was to evaluate the outcomes in this cohort of patients following colorectal surgery. METHODS: The PubMed, Embase and Cochrane databases and references were searched up to October 2022 using the PRISMA guidelines. The data collated included: patient demographics, pathology or type of colorectal operation performed, severity of liver cirrhosis, post-operative complication rates, mortality rates and prognostic factors. A quality assessment of included studies was performed with the Newcastle-Ottawa scale. RESULTS: Sixteen studies reporting the outcomes of colorectal surgery in patients with liver cirrhosis were identified, including the results of 8646 patients. The indications, pathologies and/or type of operations varied. The overall complication rate ranged from 29 to 75%, minor complication ranged 14.5-37% and major complication ranged 6.7-59.3%. The mortality rates ranged from 0 to 37%. CONCLUSION: Colorectal surgery in patients with liver cirrhosis still carries considerable morbidity and mortality rates. This group of patients needs to be managed in a multidisciplinary setting to achieve excellent outcomes. Future research should focus on uniform definitions to enable interpretable outcomes.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".