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Record W2034094501 · doi:10.3747/co.v17i0.703

Prevention Strategies in Prostate Cancer

2010· article· en· W2034094501 on OpenAlexaffvenue
Greg Trottier, Nathan Lawrentschuk, Neil Fleshner

Bibliographic record

VenueCurrent Oncology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineDutasterideProstate cancerClinical trialCancer preventionIntensive care medicineCancerPrimary preventionOncologyInternal medicineDisease

Abstract

fetched live from OpenAlex

Prostate cancer (PCa) prevention has been an exciting and controversial topic since the results of the Prostate Cancer Prevention Trial (PCPT) were published. With the recently published results of the reduce (Reduction by Dutasteride of Prostate Cancer Events) trial, interest in this topic is at a peak. Primary pca prevention will be unlikely to affect mortality significantly, but the reduction in overtreatment and the effect on quality of life from the avoidance of a cancer diagnosis are important factors to consider.This review provides a comparative update on the REDUCE and PCPT trials and some clinical recommendations. Other potential primary preventive strategies with statins, selective estrogen response modulators, and nutraceutical compounds-including current evidence for these agents and their roles in clinical practice-are discussed. Many substances that have been examined in the primary prevention of pca and for which clinical data are either negative or particularly weak are not covered.The future of PCa prevention continues to expand, with several ongoing clinical trials and much interest in tertiary prostate cancer 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 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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.940
Threshold uncertainty score0.393

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.039
GPT teacher head0.396
Teacher spread0.357 · 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
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".

Quick stats

Citations16
Published2010
Admission routes2
Has abstractyes

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