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Record W2342783650 · doi:10.3233/blc-169007

Collaborating to Move Research Forward: Proceedings of the 10th Annual Bladder Cancer Think Tank

2016· article· en· W2342783650 on OpenAlexaff
Ashish M. Kamat, Piyush K. Agarwal, Trinity J. Bivalacqua, Stephanie Chisolm, Siamak Daneshmand, James H. Doroshow, Jason A. Efstathiou, Matthew D. Galsky, Gopa Iyer, Wassim Kassouf, Jay B. Shah, John A. Taylor, Stephen B. Williams, Diane Zipursky Quale, Jonathan E. Rosenberg

Bibliographic record

VenueBladder Cancer · 2016
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsBladder cancerMultidisciplinary approachVariety (cybernetics)Multidisciplinary teamMedicineMedical educationPublic relationsCancerPsychologyEngineering ethicsPolitical scienceEngineeringComputer scienceNursingArtificial intelligenceInternal medicine

Abstract

fetched live from OpenAlex

The 10th Annual Bladder Cancer Think Tank was hosted by the Bladder Cancer Advocacy Network and brought together a multidisciplinary group of clinicians, researchers, representatives and Industry to advance bladder cancer research efforts. Think Tank expert panels, group discussions, and networking opportunities helped generate ideas and strengthen collaborations between researchers and physicians across disciplines and between institutions. Interactive panel discussions addressed a variety of timely issues: 1) data sharing, privacy and social media; 2) improving patient navigation through therapy; 3) promising developments in immunotherapy; 4) and moving bladder cancer research from bench to bedside. Lastly, early career researchers presented their bladder cancer studies and had opportunities to network with leading experts.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.364
Teacher spread0.329 · 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 designNot applicable
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

Citations4
Published2016
Admission routes1
Has abstractyes

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