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Recent trends in prostate cancer in Canada

2019· article· en· W2989330021 on OpenAlexaffabout
Allana G. LeBlanc, Alain Demers, Amanda Shaw

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsProstate cancerMedicineIncidence (geometry)Stage (stratigraphy)CancerCancer registryDemographyMortality rateProstate cancer screeningProstate-specific antigenProstateGynecologyGerontologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Prostate cancer is the most common type of cancer in Canadian men. Screening recommendations have changed substantially over the last 25 years. Since 2011 (United States) and 2014 (Canada), taskforce guidelines have recommended against screening using the prostate-specific antigen (PSA) test in low-risk men of all ages. This work reports on trends in prostate cancer incidence, mortality, and stage at diagnosis in Canada from 1992 to 2015. DATA AND METHODS: Prostate cancer incidence, mortality, and stage at diagnosis were retrieved from Statistics Canada's Canadian Cancer Registry and Canadian Vital Statistics - Death Database. Joinpoint analysis was used to examine trends over time. RESULTS: The age-standardized incidence rate (ASIR) of prostate cancer peaked in 1993 and 2001, and declined thereafter. From 2011 to 2015, the ASIR declined by 9.3% per year. The age-standardized mortality rate (ASMR) decreased continuously from 1992 to 2015, but fell most rapidly (2.9% per year) after 2001. Data from two provinces show that, from 2005 to 2015, the rate of Stage I and Stage II cancers decreased by 3.2% per year, while the rate of Stage III and Stage IV cancers remained relatively stable. DISCUSSION: Incidence of prostate cancer has declined substantially in recent years. Most of the decline seems to be in localized cases (Stage I and Stage II). Changes in incidence have mirrored changes to PSA screening recommendations. Future work should continue to monitor trends over time at the national level, especially as they relate to screening recommendations.

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)0.005
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: none
Teacher disagreement score0.060
Threshold uncertainty score0.437

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0070.017
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.017
GPT teacher head0.242
Teacher spread0.225 · 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".

Quick stats

Citations30
Published2019
Admission routes2
Has abstractyes

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