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Record W4413097123 · doi:10.1016/j.lana.2025.101180

Is it time for Canada to revisit its approach to prostate cancer screening?

2025· review· en· W4413097123 on OpenAlexafffundabout
David‐Dan Nguyen, Aïsha Lofters, Christopher J.D. Wallis, Alexandre R. Zlotta, Neil Fleshner, Quoc-Dien Trinh, Antonio Finelli, Laura C. Rosella, Allan S. Detsky, Monique J. Roobol, Girish S. Kulkarni

Bibliographic record

VenueThe Lancet Regional Health - Americas · 2025
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreMount Sinai HospitalWomen's College HospitalPublic Health OntarioUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of TorontoMovember Foundation
KeywordsProstate cancerCancerMedicineOncologyInternal medicine

Abstract

fetched live from OpenAlex

Prostate cancer is the third leading cause of cancer death among Canadian men. Despite advances in the last decade mitigating overdiagnosis and overtreatment, Canadian guidelines have recommended against routine prostate-specific antigen (PSA) screening since 2014. This has resulted in opportunistic screening, marked by inequitable access, low-value testing, and missed opportunities for early detection. We review global policy developments, emerging trial data, and implementation strategies, which suggest that organised, risk-stratified screening may improve outcomes and equity. However, overdiagnosis and associated harms remain a concern within organised programs. To address this uncertainty and generate timely, policy-relevant evidence, we propose implementing population-wide, adaptive platform trials embedded in the healthcare system. This design would enable real-time integration of new technologies, standardised protocols, and equitable access-hallmarks of a learning healthcare system. Such a model could help Canada modernise prostate cancer screening while carefully weighing benefits, harms, and equity in a rapidly evolving landscape.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.412
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
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 head0.195
GPT teacher head0.455
Teacher spread0.261 · 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.

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

Citations1
Published2025
Admission routes3
Has abstractyes

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