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Record W2612201852 · doi:10.1200/jop.2017.022137

Evolving Treatment of Advanced Urothelial Cancer

2017· review· en· W2612201852 on OpenAlexaff
Srikala S. Sridhar

Bibliographic record

VenueJournal of Oncology Practice · 2017
Typereview
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineBladder cancerTolerabilityCisplatinOncologyCystectomyInternal medicineCancerDiseaseClinical trialUrothelial cancerChemotherapyIntensive care medicineAdverse effect

Abstract

fetched live from OpenAlex

Urothelial cancer of the bladder is a smoking-related cancer and the fifth most common cancer in the United States. At presentation, up to 25% of patients will have muscle-invasive disease and, despite cystectomy or bladder-sparing trimodality approaches, will develop metastatic disease. Cisplatin-based combination chemotherapy regimens remain the standard of care in first-line metastatic disease. Although response rates to these regimens are high, they are rarely durable, and median overall survival is only 12 to 15 months. Treatment options following progression on cisplatin-based regimens or for patients unfit for cisplatin due to poor performance status, impaired renal function, or comorbidities have been quite limited. However, there is now a new class of drugs known as immune checkpoint inhibitors, which target the programmed cell death 1/programmed cell death-ligand 1 axis and promote antitumor immunity, that are showing both efficacy and tolerability. These drugs have now been approved for use in both cisplatin-treated and most recently cisplatin-unfit patients. Clinical trials are currently ongoing to determine how best to use these drugs and whether they should be used alone or in combination with other treatments. This review will discuss the current standard of care in the management of urothelial cancer and highlight recent trials of immunotherapy in this disease.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.175
GPT teacher head0.521
Teacher spread0.347 · 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

Citations21
Published2017
Admission routes1
Has abstractyes

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