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Record W4412724750 · doi:10.1080/14796694.2025.2535280

SGNDV-001: disitamab vedotin with pembrolizumab in HER2-expressing locally advanced or metastatic urothelial carcinoma

2025· article· en· W4412724750 on OpenAlexaff
Thomas Powles, Enrique Grande, Nimira Alimohamed, Niara Oliveira, Srikala S. Sridhar, Alexandra Drakaki, Ravindran Kanesvaran, Yohann Loriot, Andrea Necchi, Sonia Franco, Dingfeng Jiang, Kristel Apolinario, Wei Zhang, Matthew D. Galsky

Bibliographic record

VenueFuture Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Calgary
FundersSeagenPfizer
KeywordsPembrolizumabMedicineOncologyInternal medicineAntibody-drug conjugateChemotherapyMetastatic Urothelial CarcinomaAntibodyImmunotherapyCancerUrothelial carcinomaMonoclonal antibodyImmunologyBladder cancer

Abstract

fetched live from OpenAlex

INTRODUCTION: Platinum-based chemotherapy for the treatment of locally advanced or metastatic urothelial carcinoma (la/mUC), has been the first-line standard of care for many decades. Enfortumab vedotin, an antibody-drug conjugate, combined with pembrolizumab, a programmed death 1 (PD-1) inhibitor, recently demonstrated improved efficacy versus chemotherapy in la/mUC. Since 60%-80% of patients with UC have tumors expressing human epidermal growth factor receptor 2 (HER2), HER2-directed vedotin-based antibody-drug conjugates may also be beneficial in la/mUC. PATIENTS AND METHODS: The phase 3 trial SGNDV-001 (C5731001; NCT05911295) is evaluating disitamab vedotin (HER2-directed antibody-drug conjugate) with pembrolizumab compared with chemotherapy in treatment-naive patients with HER2-expressing la/mUC. Dual primary endpoints are progression-free survival (per blinded independent central review) and overall survival. Potential synergistic effects of disitamab vedotin and pembrolizumab could establish this combination as a novel therapeutic option for HER2-expressing la/mUC.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.843

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 head0.013
GPT teacher head0.314
Teacher spread0.301 · 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 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

Citations6
Published2025
Admission routes1
Has abstractyes

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