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Record W4282980662 · doi:10.3390/curroncol29060341

Immunotherapy as a Neoadjuvant Therapy for a Patient with Hepatocellular Carcinoma in the Pretransplant Setting: A Case Report

2022· article· en· W4282980662 on OpenAlexvenueno aff
Maen Abdelrahim, Abdullah Esmail, Godsfavour Umoru, Kiersten Westhart, Ala Abudayyeh, Ashish Saharia, Rafik M. Ghobrial

Bibliographic record

VenueCurrent Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHepatocellular carcinomaBevacizumabAtezolizumabOncologyInternal medicineNeoadjuvant therapySystemic therapyImmunotherapyNivolumabCancerChemotherapyBreast cancer

Abstract

fetched live from OpenAlex

Systemic combination therapy of immune checkpoint inhibitors and vascular endothelial growth factors have provided the basis for improved outcomes in select patients with unresectable or metastatic hepatocellular carcinoma. However, for patients with resectable disease, surgery alone or an orthotopic liver transplant remains the standard of care. Within the realms of transplant oncology, neoadjuvant systemic therapy is currently being evaluated as a potential strategy to improve outcomes in patients with HCC. Here, we report excellent response with significant downstaging in a safe manner after neoadjuvant treatment with atezolizumab and bevacizumab in a patient diagnosed with poorly differentiated HCC. As a result of the significant response observed with safe outcomes, the patient was listed for orthotopic liver transplant (OLT) evaluation and transplanted successfully.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0020.001

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 head0.099
GPT teacher head0.334
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations51
Published2022
Admission routes1
Has abstractyes

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