MétaCan
Menu
Back to cohort
Record W4386386927 · doi:10.18632/aging.204982

Availability of living donor optimizes timing of liver transplant in high-risk waitlisted cirrhosis patients

2023· review· en· W4386386927 on OpenAlex
Fakhar Ali Qazi Arisar, Shiyi Chen, Catherine Chen, Noorulsaba Shaikh, Ravikiran S. Karnam, Wei Xu, Sumeet K. Asrani, Zita Galvin, Gideon M. Hirschfield, Keyur Patel, Cynthia Tsien, Nazia Selzner, Mark S. Cattral, Leslie Lilly, Mamatha Bhat

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAging · 2023
Typereview
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsPrincess Margaret Cancer CentreToronto General HospitalUniversity Health NetworkPublic Health OntarioUniversity of Toronto
FundersCanadian Liver Foundation
KeywordsChenMedicineCirrhosisGerontologyDemographyInternal medicineSociologyBiology

Abstract

fetched live from OpenAlex

< 0.0001) especially benefited. Our prediction model identified patients at highest risk of dropout while waiting for deceased donor and most benefiting of pLD (time-dependent area under the receiver operating characteristic curve 0.82). Access to LDLT in a transplant program can optimize the timing of transplant for the increasingly older, frail patient population with comorbidities who are at highest risk of dropout.

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.

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)0.000
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.392
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.051
GPT teacher head0.300
Teacher spread0.249 · 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