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Record W4285606368 · doi:10.1016/j.urolonc.2022.06.008

Accuracy of the CUETO, EORTC 2016 and EAU 2021 scoring models and risk stratification tables to predict outcomes in high–grade non-muscle-invasive urothelial bladder cancer

2022· article· en· W4285606368 on OpenAlexaff
Wojciech Krajewski, Júlia Aumatell, José Daniel Subiela, Łukasz Nowak, Andrzej Tukiendorf, Marco Moschini, Giuseppe Basile, Sławomir Poletajew, Bartosz Małkiewicz, Francesco Del Giudice, Martina Maggi, Benjamin I. Chung, Alessia Cimadamore, Andrea Benedetto Galosi, Rocco Francesco Delle Fave, David D’Andrea, Shahrokh F. Shariat, Jakub Horňák, Marko Babjuk, Joanna Chorbińska, Jeremy Yuen‐Chun Teoh, Tim Muilwijk, Steven Joniau, Alessandro Tafuri, Alessandro Antonelli, Andrea Panunzio, Mario Álvarez‐Maestro, Giuseppe Simone, Riccardo Mastroianni, Jan Łaszkiewicz, Chiara Lonati, Stefania Zamboni, Claudio Simeone, Łukasz Niedziela, Luigi Candela, Petr Macek, Roberto Contieri, Beatriz Gutiérrez Hidalgo, Juan Gómez Rivas, Roman Sosnowski, Keiichiro Mori, Carmen Mir, Francesco Soria, Daniel A. González‐Padilla, Ó. Rodríguez Faba, Juan Palou, Guillaume Ploussard, Paweł Rajwa, Agnieszka Hałoń, Ekaterina Laukhtina, Benjamin Pradère, Karl H. Tully, Francisco Burgos, Miguel Ángel Jiménez Cidre, Tomasz Szydełko

Bibliographic record

VenueUrologic Oncology Seminars and Original Investigations · 2022
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsMedicineBladder cancerConcordanceCohortOncologyInternal medicineRetrospective cohort studyArea under the curveRisk stratificationRisk assessmentCancer

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.008
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.028
GPT teacher head0.311
Teacher spread0.283 · 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 designObservational
DomainMethods
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

Citations28
Published2022
Admission routes1
Has abstractno

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