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Record W2344246016 · doi:10.5539/ijc.v8n2p81

Transarterial Radioembolization: A New Selection to Treat Hepatocellular Carcinoma

2016· article· en· W2344246016 on OpenAlexvenueno aff
Haipeng Yu, Changfu Liu, Tonguo Si, Xueling Yang, Weihao Zhang, Yan Xu, Yong Li

Bibliographic record

VenueInternational Journal of Chemistry · 2016
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsHepatocellular carcinomaMicrosphereLiver transplantationLiver cancerPortal veinMedicineRadiologyTransplantationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

With the development of the material science, the clinical application of stable nuclide microspheres has become a hot spot of endovascular treatment of liver cancer in last 10 years. Transarterial radioembolization came to be a new selection to treat hepatocellular carcinoma. The characteristics of the radioactive microsphere determine obvious different between TARE and TACE. For early HCC, TARE as the degradation treatment or transition treatment waiting for liver transplantation.For advanced HCC, TARE as the treatment of unresectable advanced hepatocellular cancer. TARE as the rescue treatment of recurrence after liver resection. TARE as a treatment of HCC with portal vein tumor thrombus. How to make more patients in Asia countries such as China benefit, the optimization of treatment, indications of therapy, radioactive microsphere local production, health economics studies, all needed to further research.

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)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.028
GPT teacher head0.251
Teacher spread0.223 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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