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Record W4407930482 · doi:10.1177/23523735251319185

Summary from the NCI clinical trials planning meeting on next generation of clinical trials in non-muscle invasive bladder cancer <sup/>

2025· review· en· W4407930482 on OpenAlexaff
Andrea B. Apolo, Brian C. Baumann, Hikmat Al‐Ahmadie, Leslie Ballas, Rick Bangs, Kenneth Brothers, Stephanie Cooper Greenberg, Scott E. Delacroix, James J. Dignam, Jason A. Efstathiou, Adam S. Feldman, Jared C. Foster, Noah M. Hahn, Emma Hall, Donna E. Hansel, Jean Hoffman‐Censits, Ashish M. Kamat, Sophia C. Kamran, Francesca Khani, Seth P. Lerner, Robert R. Lipman, Bhupinder Mann, David J. McConkey, Tracy L. Rose, Angela B. Smith, Catherine M. Tangen, Abdul Tawab Amiri, Chana Weinstock, Pamela West, Matthew I. Milowsky, Peter C. Black

Bibliographic record

VenueBladder Cancer · 2025
Typereview
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsUniversity of British Columbia
FundersNational Cancer Institute
KeywordsClinical trialBladder cancerMedicinePrioritizationMultidisciplinary approachTask forceGenitourinary systemCancerMedical physicsIntensive care medicineInternal medicineManagement scienceEngineering

Abstract

fetched live from OpenAlex

The National Cancer Institute organized a virtual Clinical Trials Planning Meeting (CTPM) on 'Defining the next generation of clinical trials with combination therapies in non-muscle invasive bladder cancer (NMIBC)' led by the Bladder Cancer Task Force of the NCI Genitourinary Cancers Steering Committee. The purpose of this meeting was to accelerate advances in clinical trials for patients with high-risk NMIBC. The meeting delivered a multidisciplinary expert consensus on optimal strategies for next-generation clinical trial designs in NMIBC with prioritization of combination therapies. Two clinical trial concepts were developed for potential implementation within the National Clinical Trials Network.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.022
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.898
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0130.004
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.002
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 head0.575
GPT teacher head0.562
Teacher spread0.013 · 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 designOther design
Domainnot available
GenreReview

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

Citations0
Published2025
Admission routes1
Has abstractyes

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