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Record W2619460247 · doi:10.21037/tau.2017.04.31

Role of mpMRI of the prostate in screening for prostate cancer

2017· review· en· W2619460247 on OpenAlexaff
Christopher J.D. Wallis, Masoom A. Haider, Robert K. Nam

Bibliographic record

VenueTranslational Andrology and Urology · 2017
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineProstate cancerNomogramProstateProstate cancer screeningMagnetic resonance imagingCancerStage (stratigraphy)Prostate-specific antigenOncologyProstate biopsyManagement of prostate cancerRadiologyDiseaseGynecologyInternal medicine

Abstract

fetched live from OpenAlex

Prostate cancer screening offers the opportunity to significantly reduce morbidity and mortality from this disease. Currently, serum prostate-specific antigen (PSA) testing is the most widely used screening modality. However, PSA testing continues to have low positive and negative predictive value leading to unnecessary invasive prostate biopsy while missing patients with aggressive forms of the disease. Magnetic resonance imaging (MRI) has been gaining an increasingly large role in the management of patients with early stage prostate cancer including diagnosis in patients with abnormal PSA levels, monitoring of patients on active surveillance, and staging prior to definitive interventions. MRI-based prostate cancer risk assessment has been shown to better distinguish between clinically-significant and insignificant tumors than PSA testing alone or from nomograms. Preliminary data indicate that, among unselected patients, MRI outperforms PSA in the identification of patients with clinically significant prostate cancer. Further work is needed to examine the role of mpMRI in prostate cancer screening.

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)0.000
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 score0.951
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.070
GPT teacher head0.372
Teacher spread0.302 · 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

Citations29
Published2017
Admission routes1
Has abstractyes

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