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Record W4377220368 · doi:10.1007/s00268-023-07069-3

Colorectal Surgery in Patients with Liver Cirrhosis: A Systematic Review

2023· review· en· W4377220368 on OpenAlexaboutno aff
Zi Qin Ng, Mary Theophilus

Bibliographic record

VenueWorld Journal of Surgery · 2023
Typereview
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCirrhosisColorectal surgeryAbdominal surgeryComplicationCardiothoracic surgeryMortality rateVascular surgeryGeneral surgeryCardiac surgeryInternal medicineSurgery

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.293
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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