MétaCan
Menu
Back to cohort
Record W2911779914 · doi:10.1002/lt.25421

A Multidisciplinary Approach to Pretransplant and Posttransplant Management of Cystic Fibrosis–Associated Liver Disease

2019· article· en· W2911779914 on OpenAlexaff
A. Jay Freeman, Zachary M. Sellers, George Mazariegos, Andrea Kelly, Lisa Saiman, George B. Mallory, Simon C. Ling, Michael R. Narkewicz, Daniel H. Leung

Bibliographic record

VenueLiver Transplantation · 2019
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversity of Toronto
FundersBristol-Myers Squibb FoundationCystic Fibrosis Foundation
KeywordsMedicineCystic fibrosisLiver transplantationPortal hypertensionLiver diseaseInternal medicineIntensive care medicineDiseaseGastroenterologyTransplantationCirrhosis

Abstract

fetched live from OpenAlex

Approximately 5%-10% of patients with cystic fibrosis (CF) will develop advanced liver disease with portal hypertension, representing the third leading cause of death among patients with CF. Cystic fibrosis with advanced liver disease and portal hypertension (CFLD) represents the most significant risk to patient mortality, second only to pulmonary or lung transplant complications in patients with CF. Currently, there is no medical therapy to treat or reverse CFLD. Liver transplantation (LT) in patients with CFLD with portal hypertension confers a significant survival advantage over those who do not receive LT, although the timing in which to optimize this benefit is unclear. Despite the value and efficacy of LT in selected patients with CFLD, established clinical criteria outlining indications and timing for LT as well as disease-specific transplant considerations are notably absent. The goal of this comprehensive and multidisciplinary report is to present recommendations on the unique CF-specific pre- and post-LT management issues clinicians should consider and will face.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.012
GPT teacher head0.264
Teacher spread0.251 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations32
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueLiver TransplantationSame topicCystic Fibrosis Research AdvancesFrench-language works237,207