MétaCan
Menu
Back to cohort
Record W6946015985 · doi:10.25934/pr00008602

Statin use and prostate cancer outcomes in patients treated in the ARAMIS trial

2025· dataset· en· W6946015985 on OpenAlexaffabout

Bibliographic record

VenueVivli · 2025
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer Centre
Fundersnot available
KeywordsProstate cancerStatinConcomitantDiseaseCancerTestosterone (patch)Prostate

Abstract

fetched live from OpenAlex

Prostate cancer (PCa) remains the most commonly diagnosed disease in Canadian men and is the third leading cause of cancer-related death. In 2016, an estimated 21,600 men were diagnosed with prostate cancer and 4000 men died from the disease. Men with PCa are usually elderly and tend to have other concomitant diseases like high blood pressure, high cholesterol, prior stroke etc. Hence, in addition to their medications to manage PCa, a vast majority require additional medicines like statins. Statins are a group of drugs that act to reduce levels of fats, including triglycerides and cholesterol, in the blood. Statins may also reduce inflammation in the artery walls, which could prevent blockages that damage organs such as the heart and brain. There is increasing interest in statin medications as inhibitors of PCa development and growth. Studies have shown positive associations between statin use and PCa outcomes, though nearly all the studies have been "retrospective" meaning the data had already been collected (perhaps for another reason) and researchers went back to look at the statin-cancer associations. Metastatic castrate resistant prostate cancer (mCRPC) is a prostate cancer that has spread to other parts of the body, and which keeps growing even when the amount of male hormones in the body are reduced to very low levels. Reducing the male hormones, called androgens, in the body is essential to stop them from fueling prostate cancer cell growth. This can be achieved by either by surgically removing the testes and/or by using newer medicines called Androgen receptor-axis-targeted therapies (ARATs). Our proposal specifically looks at Darolutamide, an ARAT, and statin use because Darolutamide is a newer medicine offering efficacy in controlling PCa and least toxic compared to other ARATs. In mCRPC disease, the data regarding statins and outcomes are conflicting. It may be that in earlier disease settings, where survival times are longer, more of a benefit attributable to statin use would be observed. A few mechanisms have been suggested that statins may heighten novel androgen receptor inhibitors like darolutamide, including: hampering of steroid hormone production by the tumor itself, inhibition of production of other elements of the cholesterol pathway, as well as inhibition of androgens (male hormone) being brought into the cancer cells. Androgens serve as food for the prostate cancer cells to grow. Recently our group showed giving men with statins prior to surgical removal of prostate lead to signs that the cancer cells were dying and this was more pronounced in men on statins for longer periods. Darolutamide is the newest and least toxic ARAT approved for delaying metastasis and overall survival in non-metastatic castration sensitive prostate cancer (nmCRPC). nmCRPC is a diverse disease state where the cancer hasn't spread, male hormone testosterone is not detected in blood with a confirmed rising prostate-specific androgen (PSA) level. PSA test is used to monitor men after surgery or radiation therapy for prostate cancer to see if their cancer has come back. To date, no prospective clinical trials in PCa have evaluated statin use in combination with other anti-cancer treatments, although our lab remains very interested in the hypothesis of synergy between statins and other drugs that may influence the cholesterol metabolism pathway and other prostate cancer therapies (including androgen axis inhibitors like darolutamide, the subject of the proposed study).

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.321
Teacher spread0.294 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueVivliFrench-language works237,207