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Record W4391309996 · doi:10.1055/s-0043-1777462

Rapid liver regeneration following PVE/HVE improves overall survival compared to PVE alone – A midterm analysis of the multicenter DRAGON 0 cohort

2024· article· en· W4391309996 on OpenAlexaff
Remon Korenblik, Jan Heil, Jens Smits, S. James, Wolf O. Bechstein, Marc H.A. Bemelmans, Christoph A. Binkert, Stefan Breitenstein, Michael D. Williams, Olivier Detry, Maxime Dewulf, Alexandra Dili, Lukasz F. Grochola, Daniel Heise, Jennifer Kalil, Peter Metrakos, Ulf P. Neumann, Sam G. Pappas, Francesca Pennetta, Andreas A. Schnitzbauer, Jordan Tasse, Björn Winkens, Steven W.M. Olde Damink, Christiaan van der Leij, Erik Schadde, Ronald M. van Dam

Bibliographic record

VenueZeitschrift für Gastroenterologie · 2024
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineCohortRegeneration (biology)Survival analysisOncologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Background Hypertrophy-inducing procedures, such as portal vein embolization (PVE), improve future liver remnant (FLR) volume and function and help overcome limitations to resection. Mainly due to tumor progression while awaiting sufficient liver growth, 30-40% fail to achieve surgery after PVE. In the DRAGON 0 study, simultaneous portal and hepatic vein embolization (PVE/HVE) has shown to increase FLR-hypertrophy, kinetic growth rate and resectability substantially compared to PVE alone. The purpose of this study is to compare the 3-year overall survival after PVE/HVE versus PVE in patients undergoing liver resection for primary and secondary cancers of the liver.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.022
GPT teacher head0.292
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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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