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Record W4393334202 · doi:10.1016/j.eururo.2024.03.014

PRECISE Version 2: Updated Recommendations for Reporting Prostate Magnetic Resonance Imaging in Patients on Active Surveillance for Prostate Cancer

2024· article· en· W4393334202 on OpenAlexaff
Cameron Englman, Davide Maffei, Clare Allen, Alex Kirkham, Peter C. Albertsen, Veeru Kasivisvanathan, Ronaldo Hueb Baroni, Alberto Briganti, Pieter De Visschere, Louise Dickinson, Juan Gómez Rivas, Masoom A. Haider, Claudia Kesch, Stacy Loeb, Katarzyna J. Macura, Daniel Margolis, Anita Mitra, Anwar R. Padhani, Valeria Panebianco, Peter A. Pinto, Guillaume Ploussard, Philippe Puech, Andrei S. Purysko, Jan Philipp Radtke, Antti Rannikko, Art Rastinehad, Raphaële Renard‐Penna, Francesco Sanguedolce, Lars Schimmöller, Ivo G. Schoots, Shahrokh F. Shariat, Nicola Schieda, Clare M. Tempany, Barış Türkbey, Massimo Valério, Arnauld Villers, Jochen Walz, Tristan Barrett, Francesco Giganti, Caroline M. Moore

Bibliographic record

VenueEuropean Urology · 2024
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsUniversity of OttawaSinai Health SystemUniversity of Toronto
FundersCRIS Cancer FoundationProstate Cancer UKEAU Research FoundationNational Institute for Health and Care ResearchProstate Cancer Foundation
KeywordsMedicineProstate cancerProstateMagnetic resonance imagingMedical physicsRadiologyCancerGynecologyUrologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVE: The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) recommendations standardise the reporting of prostate magnetic resonance imaging (MRI) in patients on active surveillance (AS) for prostate cancer. An international consensus group recently updated these recommendations and identified the areas of uncertainty. METHODS: A panel of 38 experts used the formal RAND/UCLA Appropriateness Method consensus methodology. Panellists scored 193 statements using a 1-9 agreement scale, where 9 means full agreement. A summary of agreement, uncertainty, or disagreement (derived from the group median score) and consensus (determined using the Interpercentile Range Adjusted for Symmetry method) was calculated for each statement and presented for discussion before individual rescoring. KEY FINDINGS AND LIMITATIONS: Participants agreed that MRI scans must meet a minimum image quality standard (median 9) or be given a score of 'X' for insufficient quality. The current scan should be compared with both baseline and previous scans (median 9), with the PRECISE score being the maximum from any lesion (median 8). PRECISE 3 (stable MRI) was subdivided into 3-V (visible) and 3-NonV (nonvisible) disease (median 9). Prostate Imaging Reporting and Data System/Likert ≥3 lesions should be measured on T2-weighted imaging, using other sequences to aid in the identification (median 8), and whenever possible, reported pictorially (diagrams, screenshots, or contours; median 9). There was no consensus on how to measure tumour size. More research is needed to determine a significant size increase (median 9). PRECISE 5 was clarified as progression to stage ≥T3a (median 9). CONCLUSIONS AND CLINICAL IMPLICATIONS: The updated PRECISE recommendations reflect expert consensus opinion on minimal standards and reporting criteria for prostate MRI in AS.

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)0.087
metaresearch head score (Gemma)0.225
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.913
Threshold uncertainty score0.462

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0870.225
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.006
Bibliometrics0.0080.006
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0060.005
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0100.008

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 head0.019
GPT teacher head0.303
Teacher spread0.284 · 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
DomainReporting
GenreMethods

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

Citations58
Published2024
Admission routes1
Has abstractyes

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