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Record W2052244071 · doi:10.5430/jst.v4n2p25

Systemic therapy for bladder cancer – a medical oncologist’s perspective

2014· article· en· W2052244071 on OpenAlexvenueno aff
Benjamin A. Teply, Jaeyeon Kim

Bibliographic record

VenueJournal of Solid Tumors · 2014
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsMedicineGemcitabineOncologyBladder cancerTolerabilityCisplatinCystectomyVinblastineChemotherapyInternal medicineSystemic therapyCancerAdverse effect

Abstract

fetched live from OpenAlex

Advanced bladder cancer, both muscle-invasive localized disease and metastatic disease, is managed with systemic chemotherapy. Cisplatin-based multi-agent chemotherapy remains the cornerstone for systemic therapy. MVAC (methotrexate-vinblastine-doxorubicin-cisplatin) has been most rigorously studied, both neoadjuvantly and for palliation of metastatic disease. For metastatic disease, cisplatin-gemcitabine (GC) has compared favorably to MVAC due to improved tolerability with similar efficacy. GC has been adopted as standard therapy. Neoadjuvant chemotherapy for muscle-invasive bladder cancer improves survival among those patients eligible to receive cisplatin. Adjuvant chemotherapy is difficult to administer effectively given morbidity of radical cystectomy, and studies have shown mixed results about its benefit. Non-cisplatin regimens have been investigated but remain experimental and reserved for those not candidates for cisplatin in the metastatic setting. While multiple agents have been studied after metastatic disease progression after cisplatin-based therapy, there remain no FDA-approved therapies for the second line. Future trials with anti-VEGF therapy and immunotherapy are actively being investigated. This review examines the systemic therapy available to oncologists with current evidence and future directions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

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

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 head0.028
GPT teacher head0.366
Teacher spread0.338 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations27
Published2014
Admission routes1
Has abstractyes

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