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Record W2954147811 · doi:10.1016/j.euo.2019.06.002

Can Biomarkers Guide the Use of Neoadjuvant Chemotherapy in T2 Bladder Cancer?

2019· article· en· W2954147811 on OpenAlexaff
H. Barton Grossman, Joaquim Bellmunt, Peter C. Black

Bibliographic record

VenueEuropean Urology Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsUniversity of British Columbia
FundersRocheMerck
KeywordsCystectomyMedicineBladder cancerChemotherapyCisplatinOncologyGuidelineCancerNeoadjuvant therapyInternal medicineClinical trialDNA Damage RepairDNA repairPathologyGeneBreast cancer

Abstract

fetched live from OpenAlex

Current guidelines recommend cisplatin-based neoadjuvant chemotherapy prior to radical cystectomy as the preferred treatment of muscle-invasive bladder cancer. Nevertheless, for multiple reasons compliance with this guideline recommendation is low. This is particularly evident in clinical T2 bladder cancer, where controversy exists regarding the role of proceeding with radical cystectomy alone. Novel biomarkers such as molecular phenotype and DNA damage repair and response gene alterations may be able to predict who will respond to cisplatin-based neoadjuvant chemotherapy. This clinical problem is discussed, and a recommendation is made given the current state of the art. PATIENT SUMMARY: Neoadjuvant chemotherapy improves survival for patients with muscle-invasive bladder cancer. In the future, perhaps validated biomarkers may predict who should and should not receive this treatment.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.037
GPT teacher head0.312
Teacher spread0.275 · 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

Citations18
Published2019
Admission routes1
Has abstractyes

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