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Record W4280579488 · doi:10.1016/j.euf.2022.04.014

T1G1 Bladder Cancer: Prognosis for this Rare Pathological Diagnosis Within the Non–muscle-invasive Bladder Cancer Spectrum

2022· article· en· W4280579488 on OpenAlexaff
Irene Beijert, Anouk E. Hentschel, Johannes Bründl, Éva Compérat, Karin Plass, Óscar Rodríguez, José Daniel Subiela, Virginia Hernández, Enrique de la Peña, Isabel Alemany, Diana Turturica, Francesca Pisano, Francesco Soria, Otakar Čapoun, Lenka Bauerová, Michael Pešl, H.M. Bruins, Willemien Runneboom, Sonja Herdegen, Johannes Breyer, A. Brisuda, Ana Calatrava, J. Rubio‐Briones, Maximilian Seles, Sebastian Mannweiler, Judith Bosschieter, V.R.M. Kusuma, David Ashabere, Nicolai Huebner, Juliette Cotte, Laura S. Mertens, A. Masson-Lecomte, Fredrik Liedberg, Daniel L. Cohen, Luca Lunelli, Olivier Cussenot, Dimitrios Volanis, Jean‐François Côté, Morgan Rouprêt, Andrea Haitel, Shahrokh F. Shariat, Hugh Mostafid, Jakko A. Nieuwenhuijzen, Richard Zigeuner, José L. Domínguez-Escrig, Jaromír Háček, Alexandre R. Zlotta, Maximilian Burger, Matthias Evert, Christina A. Hulsbergen‐van de Kaa, Antoine G. van der Heijden, Lambertus A. Kiemeney, Viktor Soukup, Luca Molinaro, Paolo Gontero, Carlos Llorente, Ferrán Algaba, Joan Palou, James N’Dow, María J. Ribal, Theodorus van der Kwast, Marko Babjuk, Richard Sylvester, Bas W.G. van Rhijn

Bibliographic record

VenueEuropean Urology Focus · 2022
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineBladder cancerPathologicalStage (stratigraphy)Incidence (geometry)Internal medicineProportional hazards modelCancerOncologyUrologyCumulative incidenceGastroenterologyCohort

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)0.000
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.026
GPT teacher head0.278
Teacher spread0.252 · 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

Citations7
Published2022
Admission routes1
Has abstractno

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