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Impact of putative chemopreventative agents on prostate cancer diagnosis.

2019· article· en· W4236922292 on OpenAlexaffabout
Hanan Goldberg, Faizan Moshin, Zachary Klaassen, Thenappan Chandrasekar, Christopher Wallis, Jaime O. Herrera‐Cáceres, Ardalan Ahmed, Teck Sing Woon, Girish S. Kulkarni, Alejandro Berlín, Refik Saskin, Robert J. Hamilton, Shabbir M.H. Alibhai, Neil Fleshner

Bibliographic record

VenueJournal of Clinical Oncology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity Health NetworkPrincess Margaret Cancer CentrePublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsMedicineProstate cancerInternal medicineProportional hazards modelComorbidityDiabetes mellitusCancerMetforminPopulationRetrospective cohort studyCancer registryEndocrinologyInsulin

Abstract

fetched live from OpenAlex

40 Background: Prostate cancer (PC) is the most common non-cutaneous cancer in Canadian men and the third most common cause of cancer death in Canada. Several studies have shown that use of commonly prescribed medications, including those used for diabetes and hypercholesterolemia, is associated with improved survival in various malignancies, including PC. There has not been any large population-based study, examining the effects of these and other commonly prescribed medications, on the rate of PC diagnosis, over a 20 years follow-up period. Methods: A retrospective population-based study using data from the institute of clinical evaluative sciences, including all male patients aged 65 and above in Ontario who have had a negative first prostate biopsy between 1994 and 2016. We assessed the impact of commonly prescribed medications on PC diagnosis. The medications included Statins (hydrophilic and hydrophobic), diabetes drugs (metformin, insulins, sulfonylureas, and thizolidinedions), proton pump inhibitors, 5 alpha reductase inhibitors, and alpha blockers. Time dependent Cox regression proportional hazards models were performed determine predictors of PC diagnosis. Medication exposure was time varying and modeled as “ever” vs. “never” use or as cumulative exposure for 6 months of usage. A priori variables included in the model included age, ADG comorbidity score, rurality index, index year, and all medications. Results: A total of 51,415 men were analyzed over a mean (SD) follow-up time of 8.06 (5.44) years. Overall, 10,466 patients (20.4%) were diagnosed with PC, 16,726 (32.5%) had died, and 1,460 (2.8%) patients died of PC. On multivariable analysis increasing age and rurality index were associated with higher PC diagnosis rate, while a more recent index year, and usage of hydrophilic statins was associated with a lower diagnosis rate in both “ever” vs. “never” and cumulative models (HR 0.832, 95% CI 0.732-0.946, p = 0.005, HR 0.973 95% CI 0.951-0.995, p = 0.016, respectively). Conclusions: Hydrophilic statins are associated with a clinically significant lower PC diagnosis. To our knowledge this is the first study demonstrating a clear advantage of one group of statins (hydrophilic) over another (hydrophobic) in PC prevention.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.064
GPT teacher head0.494
Teacher spread0.429 · 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 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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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