MétaCan
Menu
Back to cohort
Record W3126763304 · doi:10.1016/j.euf.2020.07.002

Nomogram Predicting Bladder Cancer–specific Mortality After Neoadjuvant Chemotherapy and Radical Cystectomy for Muscle-invasive Bladder Cancer: Results of an International Consortium

2020· article· en· W3126763304 on OpenAlexaff
Maria Carmen Mir, Michele Marchioni, Kamran Zargar‐Shoshtari, Adrian Fairey, Laura S. Mertens, Colin P. Dinney, Laura‐Maria Krabbe, Michael S. Cookson, Niels-Erik Jacobsen, Joshua Griffin, Jeffrey S. Montgomery, Nikhil Vasdev, Evan Y. Yu, Évanguelos Xylinas, Jonathan McGrath, Wassim Kassouf, Marc Dall’Era, Srikala S. Sridhar, Jonathan Aning, Shahrokh F. Shariat, Jonathan L. Wright, Andrew C. Thorpe, Todd M. Morgan, Jeffrey M. Holzbeierlein, Trinity J. Bivalacqua, Scott North, Daniel A. Barocas, Yair Lotan, Petros Grivas, Andrew J. Stephenson, J.B. Shah, Bas W. van Rhijn, Philippe E. Spiess, D. Daneshmand, Peter C. Black

Bibliographic record

VenueEuropean Urology Focus · 2020
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsPrincess Margaret Cancer CentreMcGill University Health CentreUniversity of AlbertaUniversity of British Columbia
Fundersnot available
KeywordsMedicineBladder cancerCystectomyHazard ratioNomogramOncologyInternal medicineProportional hazards modelStage (stratigraphy)CancerUrologyConfidence intervalChemotherapyNeoadjuvant therapyBreast 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)0.007
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
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 head0.029
GPT teacher head0.300
Teacher spread0.271 · 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 designSimulation or modeling
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

Citations33
Published2020
Admission routes1
Has abstractno

Explore more

Same venueEuropean Urology FocusSame topicBladder and Urothelial Cancer TreatmentsFrench-language works237,207