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Trends in Prostate Cancer Screening: Canada

2009· book-chapter· en· W12541329 on OpenAlexaffabout
Robert K. Nam, Laurence Klotz

Bibliographic record

VenueHumana Press eBooks · 2009
Typebook-chapter
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsProstate cancerMedicineRectal examinationProstate cancer screeningCancerContext (archaeology)GynecologyProstateNomogramProstate-specific antigenIncidence (geometry)OncologyMalignancyInternal medicine

Abstract

fetched live from OpenAlex

In Canada, prostate cancer is the most common male malignancy and the third most common cause of cancer death in males. Within a publicly funded healthcare system, government guidelines do not support prostate cancer screening with prostate-specific antigen (PSA), despite national primary care and specialist groups taking a more favorable position. Current surveys among patients and physicians indicate that the practice of prostate cancer screening is widespread. This has translated into temporal trends in the national incidence rates for prostate cancer which are similar to other constituencies which employ widespread screening. Methods of prostate cancer screening mainly consist of PSA, the free:total PSA ratio, and digital rectal examination (DRE). Nomograms based on Canadian-based cohorts are being used and evaluated in the context of a prostate cancer screening program. Factors such as age, ethnicity, family history of prostate cancer, and urinary symptoms are being incorporated in PSA evaluations to assess an individual’s risk for prostate cancer.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.057
Threshold uncertainty score0.410

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.014
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.004

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 head0.065
GPT teacher head0.296
Teacher spread0.230 · 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

Citations7
Published2009
Admission routes2
Has abstractyes

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