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Performance of the 17-gene genomic prostate score test in men with prostate cancer (PCa) managed with active surveillance (AS): Results from the Canary Prostate Active Surveillance Study (PASS).

2019· article· en· W2922100235 on OpenAlexaff
Daniel W. Lin, Yingye Zheng, Jesse K. McKenney, Marshall Brown, Ruixiao Lu, Michael Crager, Hilary Boyer, James D. Brooks, Atreya Dash, Michael D. Fabrizio, Martin Gleave, Michael A. Liss, Todd M. Morgan, Ian M. Thompson, Andrew A. Wagner, Athanasios C. Tsiatis, Andrea Pingatore, H. Jeffrey Lawrence, Peter S. Nelson, Lisa F. Newcomb

Bibliographic record

VenueJournal of Clinical Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineProstatectomyProstate cancerBiopsyProstateProportional hazards modelProstate-specific antigenUrologyHazard ratioProstate biopsyInternal medicineConfidence intervalGynecologyCancerOncology

Abstract

fetched live from OpenAlex

262 Background: The 17-gene Genomic Prostate Score (GPS) test (scale 0-100) predicts adverse surgical pathology (AP) and recurrence in newly diagnosed low- and intermediate-risk PCa. Studies of the predictive value of the GPS test in men initially managed with AS are limited. Methods: Diagnostic biopsy tissue was obtained from 634 men enrolled at 8 sites in PASS. Time to AP (Gleason Grade Group (GG) ≥3, ≥pT3a, or N1) in men who underwent radical prostatectomy (RP) was the primary endpoint. All diagnostic biopsies and RP specimens were centrally reviewed. Multivariate regression models for interval censored data were used to evaluate the association between time to AP and GPS. Inverse probability of censoring weighting was applied to adjust for informative censoring. Association between GPS and time to Gleason score upgrade on surveillance biopsy was also evaluated using a Cox Proportional Hazards model. Results: GPS results were obtained for 432 men (median follow-up 4.6 [IQR: 2.9-6.2] years); 374 and 58 with GG 1 or 2 cancer, respectively; median PSA density (PSAD) was 0.11 [IQR: 0.08-0.15]; 101 men underwent RP with central pathology after a median of 2.1 [IQR: 1.3-4.3] years surveillance, and 52 (52%) men undergoing RP had AP. 167 men upgraded at a subsequent biopsy. No clinico-pathologic covariates were significantly associated with AP other than PSAD. GPS was significantly associated with time to AP (hazards ratio [HR]/20 GPS units: 1.96 [95% CI = 1.17-4.28]; p = 0.030), when adjusted for diagnostic GG, or for dichotomous PSAD ( < vs ≥ 0.15; HR: 1.83, 95% CI = 1.04-3.62; p = 0.046). GPS was not significantly associated with AP (HR: 1.61, 95% CI = 0.87-2.98; p = 0.12) when adjusted for continuous PSAD. No association, either univariable or multivariable, was observed between GPS and subsequent biopsy upgrade. Conclusions: In a cohort of men on AS, GPS was associated with time to AP when adjusted for diagnostic GG or dichotomous PSAD. GPS was not associated with surveillance biopsy GG upgrading or AP at surgery after adjustment for continuous PSAD, although a trend was seen for AP, suggesting an association may be seen in a larger study.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.354
Teacher spread0.316 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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