− HEALTH There is a growing literature on the prediction of risk of deterioration in hospital settings, including by l…− SCIENCE Self-driving cars increasingly rely on deep neural networks to achieve human-like driving 1-3 . The opacity o…+ HEALTH Objective This study aimed to develop a mitochondrial and hematopoiesis-related differentially expressed gene…− HEALTH Axial dissections of the thoracic artery are common causes of death in people diagnosed with aortic dissectio…− HEALTH Background Enfortumab vedotin (EV) has transformed treatment for advanced urothelial carcinoma (aUC), but out…− HEALTH Background Large language model (LLM)-generated hospital courses are increasingly integrated into electronic…− HEALTH Recent years have witnessed considerable progress in computer-aided drug discovery, driven by the incorporati…+ HEALTH Background To develop a machine learning model for early differentiation of primary immune thrombocytopenia (…
TruaceTracing the truth around AIFriday, September 4, 2026
Subscribe
TRV-2026-0978Certified recordPeer-reviewed

Machine learning-integrated explainable artificial intelligence for survival prediction in urothelial carcinoma with enfortumab vedotin: an exploratory real-world analysis

Background Enfortumab vedotin (EV) has transformed treatment for advanced urothelial carcinoma (aUC), but outcomes vary. Machine learning (ML) with explainable artificial intelligence (XAI) may improve survival prediction. Methods Data from 544 aUC patients receiving EV after platinum chemotherapy and immunotherapy (51 centers, 24 countries) were analyzed. Four machine learning (ML) algorithms (Random Survival Forest, XGBoost, Elastic Net-regularized Cox, Support Vector Machine) were trained (80%) and tested (20…

Health · P Space — documented harm · certified 2026-09-04 · v1 · article view · machine-readable

Current reading — problem

Four machine learning (ML) algorithms (Random Survival Forest, XGBoost, Elastic Net-regularized Cox, Support Vector Machine) were trained (80%) and tested (20%) to predict overall survival (OS).

Evidence

Reader signal

How should this claim be treated?

Cite this record

Truvace Impact Record TRV-2026-0978, v1: “Machine learning-integrated explainable artificial intelligence for survival prediction in urothelial carcinoma with enfortumab vedotin: an exploratory real-world analysis.” Truvace, 2026-09-04. /record/TRV-2026-0978 (accessed at citation time). sha256 aaa4a5f188fa3987…

Calibration history

Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.

  1. Certifiedv1aaa4a5f188fa…

    Certified into the record

Verify this record
How to verify without trusting this page

Fetch the canonical text of any version from /api/record/TRV-2026-0978 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.