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Record W2289095651 · doi:10.14740/jocmr2496w

The Changes of HIF-1α and VEGF Expression After TACE in Patients With Hepatocellular Carcinoma

2016· review· en· W2289095651 on OpenAlexvenueno aff
Kang Liu, Juan Peng, Ke Yang, Lin Yang, Xiao Ming Zhang

Bibliographic record

VenueJournal of Clinical Medicine Research · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsnot available
FundersDepartment of Science and Technology of Sichuan Province
KeywordsMedicineHepatocellular carcinomaAngiogenesisVascular endothelial growth factorHypoxia (environmental)Hypoxia-inducible factorsVEGF receptorsInternal medicineOncologyResectionCancer researchGastroenterologySurgery

Abstract

fetched live from OpenAlex

As a common malignant tumor, hepatocellular carcinoma (HCC) has a high prevalence and is a serious threat to human health. The surgical resection rate of HCC is low, and the prognosis is poor. Although transarterial chemoembolization (TACE) is the main treatment for HCC patients who are not candidates for surgical resection, it is not considered a curative procedure. For HCC, poor TACE efficacy or TACE failure may be related to tumor angiogenesis of the residual disease. Among the many regulatory factors in tumor angiogenesis, hypoxia-inducible factor-1α (HIF-1α) and vascular endothelial growth factor (VEGF) play vital roles in this process. In this paper, we conducted a review of the dynamic change and relevance of HIF-1α and VEGF levels after TACE of HCC patients.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.106
GPT teacher head0.459
Teacher spread0.353 · 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
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

Citations142
Published2016
Admission routes1
Has abstractyes

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