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Record W4389899921 · doi:10.1097/hep.0000000000000726

Adjuvant and neoadjuvant therapies for hepatocellular carcinoma

2023· article· en· W4389899921 on OpenAlexaff
Arndt Vogel, Robert C. Grant, Tim Meyer, Gonzalo Sapisochín, Grainne M. O’Kane, Anna Saborowski

Bibliographic record

VenueHepatology · 2023
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsUniversity Health NetworkUniversity of TorontoPrincess Margaret Cancer CentreToronto General Hospital
Fundersnot available
KeywordsMedicineOncologySorafenibAdjuvantInternal medicineClinical trialAtezolizumabBevacizumabClinical endpointAdjuvant therapyHepatocellular carcinomaCancerImmunotherapyNivolumabChemotherapy

Abstract

fetched live from OpenAlex

Immune-oncology-based regimens have shown efficacy in advanced HCC and have been implemented as standard of care as first-line therapy. Their efficacy, including high response rates, and safety justify their evaluation in earlier disease stages. Following negative results for adjuvant sorafenib in the global STORM trial in 2015, 4 global phase 3 trials, featuring different immune checkpoint inhibitor combinations, entered in parallel the race in the adjuvant setting. The IMbrave050 trial, comparing adjuvant atezolizumab in combination with bevacizumab to active surveillance following curative-intent resection or ablation, was the first to report, fast-tracking the results of the first interim analysis and demonstrating an improvement in recurrence-free survival. The trial has provoked a discussion on the horizon of expectations from adjuvant treatment and the clinical relevance of efficacy endpoints. Moreover, major pathological responses reported from early phase 2 data in the neoadjuvant setting provide a strong rationale for the evaluation of these concepts in phase 3 trials. In this review, we summarize current evidence and outline future directions for systemic therapies in early-stage HCC.

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 score0.526
Threshold uncertainty score0.806

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 head0.078
GPT teacher head0.276
Teacher spread0.198 · 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

Citations37
Published2023
Admission routes1
Has abstractyes

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