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Record W4415619512 · doi:10.1007/s00345-025-05992-9

Real-world impact of cisplatin-based neoadjuvant chemotherapy on bladder cancer survival: a 20-year study

2025· article· en· W4415619512 on OpenAlexaff
Mario de Angelis, Francesco Pellegrino, Pietro Scilipoti, Mattia Longoni, Alfonso Santangelo, José Daniel Subiela, Roberto Contieri, Luca Afferi, Stefania Zamboni, Nazareno Suardi, Gennaro Musi, Stefano Luzzago, David D’Andrea, Ekaterina Laukhtina, Francesco Soria, Paolo Gontero, Francesco Del Giudice, Muhammad Shamim Khan, Ramesh Thurairaja, Morgan Roupret, Élisabeth Grobet-Jeandin, Arthur Baudewyns, Hajime Tanaka, Shunya Matsumoto, Yasuhisa Fujii, Flavia Proietti, Giuseppe Simone, Gerald Bastian Schulz, Nikolaos Pyrgidis, Guillaume Ploussard, Riccardo Bertolo, M. Roumiguié, A. Bajeot, Maria Carmen Mir, Paolo Umari, Jeremy Yuen‐Chun Teoh, Chris Ho Ming Wong, Laura S. Mertens, Renate Pichler, Keiichiro Mori, Aleksander Ślusarczyk, Cédric Poyet, Simone Albisinni, Atiqullah Aziz, Alessandro Volpe, Shahrokh F. Shariat, Benjamin Pradère, Pierre I. Karakiewicz, Stefano Resca, Edoardo Beatrici, R. P. de Groote, Alexandre Mottrie, Andrea Necchi, Francesco Montorsi, Alberto Briganti, Marco Moschini

Bibliographic record

VenueWorld Journal of Urology · 2025
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsUniversité de MontréalMcGill University Health Centre
Fundersnot available
KeywordsBladder cancerChemotherapyNephrologyRetrospective cohort studyOverall survivalUrothelial cancer

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)0.002
metaresearch head score (Gemma)0.004
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.374
Teacher spread0.346 · 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

Citations5
Published2025
Admission routes1
Has abstractno

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