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Record W2136006080 · doi:10.25011/cim.v34i6.15889

Targeting the oncogene eIF4E in cancer: From the bench to clinical trials

2011· article· en· W2136006080 on OpenAlexafffundvenue
Katherine L. B. Borden

Bibliographic record

VenueClinical and investigative medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsInstitute for Research in Immunology and Cancer
FundersDalhousie UniversityJewish General HospitalMcMaster University
KeywordsEIF4EMedicineMyeloid leukemiaCancerClinical trialTranslation (biology)OncogeneCancer researchBench to bedsideEukaryotic translationRibavirinOncologyImmunologyInternal medicineBiologyGeneCell cycleMessenger RNAGeneticsVirus

Abstract

fetched live from OpenAlex

Identifying and targeting specific oncogenes, with the hope that the resultant therapies may eventually prove to exert positive clinical effects, is a major effort in the area of cancer therapeutics. The eukaryotic translation initiation factor, eIF4E, is overexpressed in many cancers, including acute myeloid leukemia. The role of eIF4E in oncogenic transformation and the development of a means to directly target its activity with ribavirin are discussed here. Results from early stage clinical trials and factors contributing to the development of clinical resistance to ribavirin are also described.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.079
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.079
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.010
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.589
GPT teacher head0.507
Teacher spread0.081 · 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 teacher head, not a consensus.

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

Citations25
Published2011
Admission routes3
Has abstractyes

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