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Record W3083506235 · doi:10.1158/1078-0432.ccr-20-1967

Statins as Anticancer Agents in the Era of Precision Medicine

2020· review· en· W3083506235 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueClinical Cancer Research · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health ResearchTerry Fox Research InstituteCanadian Cancer Society
KeywordsMevalonate pathwayMedicineHMG-CoA reductaseStatinLimitingCancerPharmacologyReductasePrecision medicineClinical trialBioinformaticsOncologyInternal medicineEnzymeBiologyPathology

Abstract

fetched live from OpenAlex

Statins are widely prescribed cholesterol-lowering drugs that inhibit HMG-CoA reductase (HMGCR), the rate-limiting enzyme of the mevalonate metabolic pathway. Multiple lines of evidence indicate that certain cancers depend on the mevalonate pathway for growth and survival, and, therefore, are vulnerable to statin therapy. However, these immediately available, well-tolerated, and inexpensive drugs have yet to be successfully repurposed and integrated into cancer patient care. In this review, we highlight recent advances and outline important considerations for advancing statins to clinical trials in 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.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.856
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.381
GPT teacher head0.613
Teacher spread0.232 · 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