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Record W2078214711 · doi:10.2147/ott.s38403

β-blockers: a novel class of antitumor agents

2012· article· en· W2078214711 on OpenAlexaff
Yi Ji, Chen, Xiao Xiao, Shan, Kai Kai

Bibliographic record

VenueOncoTargets and Therapy · 2012
Typearticle
Languageen
FieldMedicine
TopicCancer, Stress, Anesthesia, and Immune Response
Canadian institutionsPediatric Oncology Group
FundersFudan UniversityNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsBETA (programming language)Class (philosophy)PharmacologyMedicineChemistryComputer scienceArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

β-adrenergic signaling modulates key signaling pathways that are important for tumor-promoting processes, and numerous mechanisms of action have been elucidated. Preclinical studies have demonstrated that β-adrenergic antagonists, or β-blockers, can block multiple fundamental biologic processes underlying the progression and metastasis of tumors, including the inhibition of cell proliferation, migration, invasion, resistance to programmed cell death, and tumor angiogenesis and metastasis. Human pharmacoepidemiologic studies suggest that β-blockers have a role in inhibiting cancer progression and metastasis in combination with standard therapies. Furthermore, a number of prospective studies have demonstrated that β-blockers are effective at halting infantile hemangioma growth. These findings shed light on the novel perspective of using β-blockers as a class of potential antitumor agents in clinical oncology.

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)0.000
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.309
Teacher spread0.266 · 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

Citations31
Published2012
Admission routes1
Has abstractyes

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