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Record W2163833439 · doi:10.1158/1078-0432.ccr-11-0998

The Insulin Receptor/Insulin-Like Growth Factor Receptor Family as a Therapeutic Target in Oncology

2012· article· en· W2163833439 on OpenAlexaff
Michaël Pollak

Bibliographic record

VenueClinical Cancer Research · 2012
Typearticle
Languageen
FieldMedicine
TopicGrowth Hormone and Insulin-like Growth Factors
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineInsulin receptorMetforminGrowth factor receptorInsulin-Like Growth Factor ReceptorClinical trialReceptor tyrosine kinaseInsulinTyrosine kinaseReceptorInsulin-like growth factorInsulin-like growth factor 1 receptorOncologyAntibodyCancerInternal medicinePharmacologyImmunologyGrowth factorInsulin resistance

Abstract

fetched live from OpenAlex

Over the past decade, encouraging preclinical and early clinical data concerning the relevance of the insulin receptor/insulin-like growth factor (IGF) receptor family to neoplasia led to ambitious clinical trial programs of more than a dozen drug candidates that target these receptors. These candidates include antireceptor antibodies, antiligand antibodies, receptor-specific tyrosine kinase inhibitors, and agents such as picropodophyllin and metformin that have novel mechanisms of action. Several recently reported phase III clinical trials of anti-IGF-I receptor antibodies have been disappointing and are sufficient to disprove the hypothesis that the antibodies tested have large favorable impacts on unselected patients with cancer. However, many of these trials were designed prior to recent insights concerning pathophysiology and predictive biomarkers. Future studies are required, but it will be important to optimize their design rather than simply repeat the approaches taken to date.

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)0.009
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0010.003

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 head0.211
GPT teacher head0.493
Teacher spread0.281 · 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; both teacher heads agree on what is shown here.

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

Citations97
Published2012
Admission routes1
Has abstractyes

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