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Record W2087532179 · doi:10.5858/arpa.2014-0219-sa

The Critical Role of the Pathologist in Determining Eligibility for Active Surveillance as a Management Option in Patients With Prostate Cancer: Consensus Statement With Recommendations Supported by the College of American Pathologists, International Society of Urological Pathology, Association of Directors of Anatomic and Surgical Pathology, the New Zealand Society of Pathologists, and the Prostate Cancer Foundation

2014· review· en· W2087532179 on OpenAlexaff
Mahul B. Amin, Daniel W. Lin, John L. Gore, John R. Srigley, Hemamali Samaratunga, Lars Egevad, Mark A. Rubin, John N. Nacey, H. Ballentine Carter, Laurence Klotz, Howard M. Sandler, Anthony L. Zietman, Stuart Holden, Rodolfo Montironi, Peter A. Humphrey, Andrew Evans, Jonathan I. Epstein, Brett Delahunt, Jesse K. McKenney, Daniel M. Berney, Thomas M. Wheeler, Arul M. Chinnaiyan, Lawrence D. True, Beatrice S. Knudsen, M. Elizabeth H. Hammond

Bibliographic record

VenueArchives of Pathology & Laboratory Medicine · 2014
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsUniversity of TorontoMcMaster UniversityTrillium Health Centre
Fundersnot available
KeywordsMedicineProstate cancerActive monitoringManagement of prostate cancerWatchful waitingIntensive care medicineMEDLINEGynecologyCancerInternal medicine

Abstract

fetched live from OpenAlex

CONTEXT: Prostate cancer remains a significant public health problem. Recent publications of randomized trials and the US Preventive Services Task Force recommendations have drawn attention to overtreatment of localized, low-risk prostate cancer. Active surveillance, in which patients undergo regular visits with serum prostate-specific antigen tests and repeat prostate biopsies, rather than aggressive treatment with curative intent, may address overtreatment of low-risk prostate cancer. It is apparent that a greater awareness of the critical role of pathologists in determining eligibility for active surveillance is needed. OBJECTIVES: To review the state of current knowledge about the role of active surveillance in the management of prostate cancer and to provide a multidisciplinary report focusing on pathologic parameters important to the successful identification of patients likely to succeed with active surveillance, to determine the role of molecular tests in increasing the safety of active surveillance, and to provide future directions. DESIGN: Systematic review of literature on active surveillance for low-risk prostate cancer, pathologic parameters important for appropriate stratification, and issues regarding interobserver reproducibility. Expert panels were created to delineate the fundamental questions confronting the clinical and pathologic aspects of management of men on active surveillance. RESULTS: Expert panelists identified pathologic parameters important for management and the related diagnostic and reporting issues. Consensus recommendations were generated where appropriate. CONCLUSIONS: Active surveillance is an important management option for men with low-risk prostate cancer. Vital to this process is the critical role pathologic parameters have in identifying appropriate candidates for active surveillance. These findings need to be reproducible and consistently reported by surgical pathologists with accurate pathology reporting.

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)0.266
metaresearch head score (Gemma)0.348
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.266
Threshold uncertainty score0.906

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2660.348
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0060.004
Science and technology studies0.0030.005
Scholarly communication0.0060.006
Open science0.0070.005
Research integrity0.0100.009
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 head0.011
GPT teacher head0.336
Teacher spread0.326 · 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.

Study designNot applicable
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

Citations125
Published2014
Admission routes1
Has abstractyes

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