MétaCan
Menu
Back to cohort
Record W2107021610 · doi:10.1503/cmaj.060955

Prostate-specific antigen in the early detection of prostate cancer

2007· review· en· W2107021610 on OpenAlexvenueaboutno aff
Ian M. Thompson, Donna P. Ankerst

Bibliographic record

VenueCanadian Medical Association Journal · 2007
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsnot available
FundersNational Cancer InstituteU.S. Department of Health and Human Services
KeywordsProstate cancerMedicineContext (archaeology)Rectal examinationProstate-specific antigenCancerProstate cancer screeningProstateOncologyOverdiagnosisGynecologyFamily historyInternal medicine

Abstract

fetched live from OpenAlex

Throughout Canada, the United States and much of Europe, prostate-specific antigen (PSA) screening for prostate cancer has proliferated over the past 2 decades, leading to dramatic increases in detection rates of prostate cancer. Although it has unquestionably led to increased detection of cancer and a migration to lower-stage and -volume tumours, it is still unknown whether PSA screening significantly reduces mortality from prostate cancer. Often thought to be dichotomous (i.e., either normal or elevated), PSA measurements actually reflect cancer risk, with the risks of cancer and of aggressive cancer increasing with the level of PSA. The recently developed risk calculator from the Prostate Cancer Prevention Trial, which integrates family history of prostate cancer, digital rectal examination findings, PSA test result, age, ethnicity, and history of a prior prostate biopsy with a negative result, allows clinicians to assess a patient's individual risk of cancer. This risk should be examined in the context of a patient's life expectancy and comorbidity as well as his concern about the possibility of prostate cancer. The terms "normal" and "elevated" as descriptors of PSA results should be abandoned.

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)0.003
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.033
GPT teacher head0.322
Teacher spread0.289 · 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 designOther design
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

Citations150
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Medical Association JournalSame topicProstate Cancer Diagnosis and TreatmentFrench-language works237,207