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Record W2315875453 · doi:10.1016/j.juro.2016.02.2820

PD08-04 VALIDATION OF A RISK CALCULATOR PREDICTING BIOPSY OUTCOME IN PROSTATE CANCER TREATED WITH ACTIVE SURVEILLANCE

2016· article· en· W2315875453 on OpenAlexaboutno aff
Daan Nieboer, Monique J. Roobol, Ewout W. Steyerberg, Chris H. Bangma

Bibliographic record

VenueThe Journal of Urology · 2016
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProstate cancerBiopsyCancerProstate biopsyCalculatorProstateGynecologyCancer detectionOncologyInternal medicine

Abstract

fetched live from OpenAlex

Introduction and objectives: Prostate cancer patients on active surveillance (AS) are followed using repeat prostate biopsies (Bx), a burdensome intervention not without risks for patients. Most Bx, indicated by clinical indicators like PSA change and/or pre-defined time since diagnosis, do not show disease progression or risk reclassification. Identifying patients at low risk of progression or reclassification may reduce the number of these unnecessary Bx. Our objective was to validate the Canary-EDRN active surveillance biopsy risk calculator, which estimates the probability of upgrading or reclassification on biopsy (gleason grade≥7 or more than 34% of biopsy cores positive for cancer) using the patients age, months since last biopsy, last observed PSA level, percentage of cores positive for cancer on last biopsy and the number of prior negative biopsies.\nMethods: We used data from the Movember Global Action Plan (GAP-3) project, a global consortium of AS studies comprising of more than 10,000 PC patients. Patients were included from the prostate cancer research international: active surveillance (PRIAS), an American cohort (from Johns Hopkins), and a Canadian cohort (from Sunnybrook). These cohorts contained 5,129 follow-up biopsies in 3,412 PC patients. Calibration of the risk calculator was assessed graphically and the discriminative ability was quantified using the area under the receiver operating characteristic curve (AUC). Clinical usefulness was assessed using decision curves analysis (DCA).\nResults: The AUC at external validation was 0.64 in PRIAS, 0.73 in the American cohort and 0.66 in the Canadian cohort. The probability of upgrading or risk reclassification was somewhat over-estimated in PRIAS and the Canadian cohort and somewhat under-estimated in the American cohort. DCA showed that the model was clinical useful in each cohort for risk thresholds between 10% and 30%.\nConclusions: The risk calculator provided moderate discrimination in 3 cohorts of the GAP-3 consortium, but showed some miscalibration. Before applying the risk calculator in clinical practice recalibration may be required.

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)0.009
metaresearch head score (Gemma)0.020
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 score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.016
GPT teacher head0.288
Teacher spread0.272 · 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
Published2016
Admission routes1
Has abstractyes

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