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Record W6940915550 · doi:10.11575/prism/27466

Prostate Specific Antigen Testing and Prostate Specific Antigen Velocity for the Screening of Prostate Cancer

2015· other· en· W6940915550 on OpenAlexfundaboutno aff

Bibliographic record

VenuePRISM (University of Calgary) · 2015
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
FundersCalgary Laboratory Services
KeywordsProstate cancerProstate-specific antigenProstateProstate biopsyPCA3Biopsy

Abstract

fetched live from OpenAlex

Prostate specific antigen (PSA) testing for the screening of prostate cancer is controversial with medical and governmental organizations issuing contradictory statements regarding its use. My research looked at the utilization of the PSA test for the screening of prostate cancer in Calgary, Alberta for 2011 and if sociodemographic factors influenced the rate of testing. I studied whether PSA velocity is better than a single PSA test in predicting prostate biopsy outcome and if sub-dividing Gleason score 7 prostate cancers improves the predictive ability of PSA tests. My research found that PSA testing does not follow official guidelines in younger men and that certain sociodemographic factors do influence the rate of PSA testing. I found that PSA velocity is not better than the PSA test in predicting prostate biopsy diagnosis and that sub-dividing Gleason score 7 prostate cancers can increase the clinical utility of the PSA test.

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)0.008
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: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.392

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.029
GPT teacher head0.206
Teacher spread0.178 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

Explore more

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