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Record W2884031435 · doi:10.3233/blc-180175

Immunology, Immunotherapy, and Translating Basic Science into the Clinic for Bladder Cancer

2018· article· en· W2884031435 on OpenAlexafffund
Molly A. Ingersoll, Xue Li, Brant A. Inman, John W. Greiner, Peter C. Black, Rosalyn M. Adam

Bibliographic record

VenueBladder Cancer · 2018
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsUniversity of British Columbia
FundersNational Institutes of HealthNational Cancer InstituteNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteAgence Nationale de la RechercheBladder Cancer Canada
KeywordsBladder cancerImmunotherapyContext (archaeology)MedicineCancerDiseasePerspective (graphical)Clinical trialCancer immunotherapyTumor microenvironmentImmune systemOncologyImmunologyInternal medicineBiologyComputer science

Abstract

fetched live from OpenAlex

The Fourth Annual Albert Institute Bladder Cancer Care and Research Symposium was held from September 14th–16th in Houston, Texas. The symposium covered a range of topics relevant to bladder cancer, including basic science aspects of immunology and immunotherapy that inform clinical management; intravesical therapy for non-muscle invasive disease; understanding the nuances of carcinoma in situ; and optimizing patient care and outcomes following therapy. The moving landscape of bladder cancer from an industry perspective was also discussed. In the following sections we discuss intrinsic and extrinsic factors, including the immune microenvironment and sex bias, in the context of bladder cancer; how these influence tumor development, progression, and treatment strategies; and how the interpretation of immune features in relation to molecular subtypes informs both treatment decisions and response. We conclude with a summary of key points that will need to be addressed to ensure best use of new knowledge in this area for improved clinical management of patients with bladder cancer.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.033
GPT teacher head0.364
Teacher spread0.331 · 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
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

Citations11
Published2018
Admission routes2
Has abstractyes

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