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Record W2971671150 · doi:10.1158/0008-5472.can-19-0803

Recent Developments and Therapeutic Strategies against Hepatocellular Carcinoma

2019· article· en· W2971671150 on OpenAlexaff
Mark Yarchoan, Parul Agarwal, Augusto Villanueva, Shuyun Rao, Laura A. Dawson, Thomas B. Karasic, Josep M. Llovet, Richard S. Finn, John D. Groopman, Hashem B. El‐Serag, Satdarshan P. Monga, Xin Wei Wang, Michael Karin, Robert E. Schwartz, Kenneth K. Tanabe, Lewis R. Roberts, Preethi H. Gunaratne, Allan Tsung, Kimberly Brown, Theodore S. Lawrence, Riad Salem, Amit G. Singal, Amy K. Kim, Atoosa Rabiee, Linda Resar, Jeffrey Meyer, Yujin Hoshida, Aiwu Ruth He, Kalpana Ghoshal, Patrick Ryan, Elizabeth M. Jaffee, Chandan Guha, Lopa Mishra, C. Norman Coleman, Mansoor M. Ahmed

Bibliographic record

VenueCancer Research · 2019
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsUniversity of Toronto
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Cancer InstituteNational Institutes of Health
KeywordsHepatocellular carcinomaMedicineLiver transplantationCirrhosisIntensive care medicineCancerSystemic therapyLiver cancerTransplantationOncologyInternal medicine

Abstract

fetched live from OpenAlex

Hepatocellular carcinoma (HCC) has emerged as a major cause of cancer deaths globally. The landscape of systemic therapy has recently changed, with six additional systemic agents either approved or awaiting approval for advanced stage HCC. While these agents have the potential to improve outcomes, a survival increase of 2-5 months remains poor and falls short of what has been achieved in many other solid tumor types. The roles of genomics, underlying cirrhosis, and optimal use of treatment strategies that include radiation, liver transplantation, and surgery remain unanswered. Here, we discuss new treatment opportunities, controversies, and future directions in managing 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 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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.175
GPT teacher head0.363
Teacher spread0.188 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations158
Published2019
Admission routes1
Has abstractyes

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