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Record W4391579712 · doi:10.1016/j.euo.2024.01.014

Benefit of Neoadjuvant Cisplatin-based Chemotherapy for Invasive Bladder Cancer Patients Treated with Radiation-based Therapy in a Real-world Setting: An Inverse Probability Treatment Weighted Analysis

2024· article· en· W4391579712 on OpenAlexaff
Ronald Kool, Alice Dragomir, Girish S. Kulkarni, Gautier Marcq, Rodney H. Breau, Michael Kim, Ionut Busca, Hamidreza Abdi, Mark Dawidek, Michael Uy, Gagan Fervaha, Fabio Cury, Nimira Alimohamed, Jonathan I. Izawa, Claudio Jeldres, Ricardo Rendon, Bobby Shayegan, Robert Siemens, Peter C. Black, Wassim Kassouf

Bibliographic record

VenueEuropean Urology Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsDalhousie UniversityUniversité de SherbrookeUniversity of TorontoWestern UniversityPrincess Margaret Cancer CentreUniversity of CalgaryQueen's UniversityMcMaster UniversityUniversity of British ColumbiaMcGill University Health CentreUniversity of OttawaOttawa HospitalUniversity Health Network
Fundersnot available
KeywordsMedicineBladder cancerLymphovascular invasionCystectomyCohortHazard ratioOncologyInternal medicineChemotherapyCisplatinGemcitabineRadiation therapyNeoadjuvant therapyCancerStage (stratigraphy)Subgroup analysisUrologyConfidence intervalBreast 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.313
Teacher spread0.288 · 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 teacher head, not a consensus.

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

Citations26
Published2024
Admission routes1
Has abstractno

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