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Record W3004106484 · doi:10.1093/ajcp/aqaa003

Prostate-Specific Antigen Test Utilization in a Major Canadian City

2020· article· en· W3004106484 on OpenAlexaffabout
Qunfeng Wang, Fang Chen, Depeng Jiang, Amin Kabani, AbdulRazaq Sokoro

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2020
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsUniversity of ManitobaManitoba Health
Fundersnot available
KeywordsProstate-specific antigenAntigenTest (biology)MedicineImmunologyInternal medicineProstate cancerBiologyCancer

Abstract

fetched live from OpenAlex

OBJECTIVES: To review the utilization of prostate-specific antigen (PSA) testing in Winnipeg, a major Canadian city, and to compare PSA testing rates between Winnipeg and Calgary, another major Canadian city of comparable size. METHODS: PSA testing results were reviewed by year and age group. We focused our studies in years 2011 and 2016, for which census demographic data are available. RESULTS: In Winnipeg, the PSA testing rates (patients with one or two PSA tests divided by the male population) showed a declining trend over years from 2008 to 2017. For almost all age groups, PSA testing rates in 2016 decreased in comparison to those in 2011. For age older than 40 years, the relative percentage decreases were 14% to 20%.In 2011, Winnipeg PSA testing rates were consistently higher than those in Calgary for all age groups. For age older than 40 years, the relative percentage differences were 36% to 50%.In addition, 41% and 40% of patients in Winnipeg who underwent PSA testing were younger than 50 years or older than 69 years in 2011 and 2016, respectively. CONCLUSIONS: PSA testing utilization may be falling short of optimal rates. There is a need to reinforce the optimal use of clinical 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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.127
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.115
GPT teacher head0.392
Teacher spread0.277 · 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 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

Citations2
Published2020
Admission routes2
Has abstractyes

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