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Record W4392599278 · doi:10.3233/blc-230082

Ensuring Successful Biomarker Studies in Bladder Preservation Clinical Trials for Non-muscle Invasive Bladder Cancer

2024· article· en· W4392599278 on OpenAlexaff
David J. McConkey, Brian C. Baumann, Stephanie Cooper Greenberg, David J. DeGraff, Scott E. Delacroix, Jason A. Efstathiou, Jared C. Foster, Susan Groshen, Edward E. Kadel, Francesca Khani, William Y. Kim, Seth P. Lerner, Trevor G. Levin, Joseph C. Liao, Matthew I. Milowsky, Joshua J. Meeks, David T. Miyamoto, Kent W. Mouw, Eugene J. Pietzak, David B. Solit, Debasish Sundi, Abdul Tawab-Amiri, Pamela West, Sara E. Wobker, Alexander W. Wyatt, Andrea B. Apolo, Peter C. Black

Bibliographic record

VenueBladder Cancer · 2024
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersNational Cancer Institute
KeywordsClinical trialBladder cancerBiomarkerMedicinePrioritizationBiomarker discoveryTranslational researchCancerIntensive care medicineOncologyInternal medicinePathologyProteomicsBiologyBusiness

Abstract

fetched live from OpenAlex

Recent technological advances have created new opportunities for performing biomarker studies within the National Cancer Institute's (NCI's) National Clinical Trials Network (NCTN) clinical trials.These new platforms yield more robust measurements when tissue and blood handling is optimized.At the same time, there is a strong interest in banking tissue and derivatives, such as DNA and RNA, for future biomarker studies using novel platforms that may emerge during the intervening time to trial completion.The NCI recently hosted a Clinical Trials Planning Meeting focused on two trial concepts for bladder preservation in patients with high-risk non-muscle invasive bladder cancer (NMIBC) and the correlative translational research to be integrated into these trials, where experts discussed prioritization for the use of patient samples, and a framework for best practices emerged.The overall goal of this meeting report is to summarize this discussion and to provide the working group's recommendations for biospecimen handling for future bladder preservation studies.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.399
GPT teacher head0.527
Teacher spread0.128 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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