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Record W4402731997 · doi:10.3390/jcdd11090296

Statins in the Cause and Prevention of Cancer: Confounding by Indication and Mediation by Rhabdomyolysis and Phosphate Toxicity

2024· review· en· W4402731997 on OpenAlexaff
Ronald B. Brown

Bibliographic record

VenueJournal of Cardiovascular Development and Disease · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRhabdomyolysisMedicineStatinCancerInternal medicineCholesterolConfoundingToxicityPharmacologyDiseaseOncology

Abstract

fetched live from OpenAlex

Statins are drugs used in cardiovascular pharmacotherapy to decrease hypercholesterolemia and lower the risk of atherosclerosis. Statins also increase the risk of rhabdomyolysis, which is often minimized in comparison with large relative risk reductions of cardiovascular disease reported in clinical trials. By contrast, absolute risk reductions of cardiovascular disease are often clinically insignificant and unreported in statin clinical trials. Additionally, cytotoxic effects of statins inhibit cancer cell proliferation and reduce cancer risk, but other studies found that statins are carcinogenic. Due to an inverse association between incidence of cancer and atherosclerosis, the indication to prescribe statins likely biases the association of statins with cancer prevention. Dietary patterns associated with atherosclerosis and cancer contain inverse amounts of cholesterol and phosphate, an essential mineral that stimulates tumorigenesis. Accordingly, lower cancer risk is associated with high dietary cholesterol intake and increased risk of atherosclerosis. Furthermore, serum is exposed to excessive inorganic phosphate that could increase cancer risk as rhabdomyolysis induced by statins releases phosphate from skeletal muscle breakdown. Increased risk of comorbid conditions associated with statins may share the mediating factor of phosphate toxicity. More research is warranted on statins in the cause and prevention of cancer.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.991
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.302
Teacher spread0.287 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

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