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Record W2042008937 · doi:10.1186/1741-7015-9-33

Understanding the benefit of metformin use in cancer treatment

2011· review· en· W2042008937 on OpenAlexafffund
Ryan J.O. Dowling, Pamela J. Goodwin, Vuk Stambolic

Bibliographic record

VenueBMC Medicine · 2011
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsMount Sinai HospitalUniversity Health NetworkOntario Institute for Cancer Research
FundersCanadian Institutes of Health ResearchMitacs
KeywordsMetforminMedicineBiguanideHyperinsulinemiaPhenforminPI3K/AKT/mTOR pathwayCancerPharmacologyInsulinType 2 diabetesDiabetes mellitusInternal medicineBioinformaticsOncologyEndocrinologySignal transductionInsulin resistance

Abstract

fetched live from OpenAlex

Biguanides have been developed for the treatment of hyperglycemia and type 2 diabetes. Recently, metformin, the most widely prescribed biguanide, has emerged as a potential anticancer agent. Epidemiological, preclinical and clinical evidence supports the use of metformin as a cancer therapeutic. The ability of metformin to lower circulating insulin may be particularly important for the treatment of cancers known to be associated with hyperinsulinemia, such as those of the breast and colon. Moreover, metformin may exhibit direct inhibitory effects on cancer cells by inhibiting mammalian target of rapamycin (mTOR) signaling and protein synthesis. The evidence supporting a role for metformin in cancer therapy and its potential molecular mechanisms of action are discussed.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
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 head0.368
GPT teacher head0.385
Teacher spread0.017 · 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 designSystematic review
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

Citations408
Published2011
Admission routes2
Has abstractyes

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