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Record W4283777976 · doi:10.1016/j.eclinm.2022.101534

Predictors of patient survival following liver transplant in non-alcoholic steatohepatitis: A systematic review and meta-analysis

2022· review· en· W4283777976 on OpenAlexafffundabout
Adam Minich, Fakhar Ali Qazi Arisar, Noor-ul Saba Shaikh, Leanne Herman, Amirhossein Azhie, Ani Orchanian‐Cheff, Keyur Patel, Sareh Keshavarzi, Mamatha Bhat

Bibliographic record

VenueEClinicalMedicine · 2022
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreToronto General HospitalUniversity Health NetworkPublic Health OntarioUniversity of Toronto
FundersIpsenToronto General and Western Hospital Foundation
KeywordsMedicineLiver transplantationInternal medicineMeta-analysisSteatohepatitisModel for End-Stage Liver DiseasePopulationCirrhosisHepatic encephalopathySystematic reviewMEDLINETransplantationLiver diseaseFatty liverDisease

Abstract

fetched live from OpenAlex

Background: Non-alcoholic steatohepatitis (NASH) is the second-leading indication for liver transplantation (LT) worldwide and is projected to become the leading indication. Our study aimed to determine clinical variables that predict post-LT survival in NASH. Methods: A systematic review and meta-analysis was performed. On June 18, 2020 and April 28, 2022, Ovid MEDLINE ALL, Ovid Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials were searched. No date limits were applied. Inclusion criteria specified the type of study and our study's population/comparison and outcome/timepoints. Pediatric, animal, retransplantation-only, and studies classifying cryptogenic cirrhosis patients with body mass index (BMI) <30 as NASH were excluded. Studies with duplicate cohorts and missing information were excluded from the meta-analysis. Studies were appraised using the Newcastle-Ottawa Scale. This study was preregistered in PROSPERO (CRD42020196915). Findings: =0·76). Interpretation: Our systematic review and meta-analysis aimed to synthesise predictive variables of mortality in LT NASH patients. Clinically, this might help to identify modifiable risk factors that can be optimized in the post-transplant setting to improve patient outcomes and optimises decision making in the resource-limited LT setting. Funding: Toronto General and Western Hospital Foundation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.579
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0200.006
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.105
GPT teacher head0.375
Teacher spread0.270 · 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.

Study designMeta-analysis
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

Citations22
Published2022
Admission routes3
Has abstractyes

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