Publications by authors named "D Bertwistle"

Article Synopsis
  • The study evaluates the Palmer algorithm, which aids in selecting flexible survival models for extrapolating long-term survival data from immuno-oncology trials.
  • The algorithm was applied to data from the CheckMate-649 trial, comparing survival estimates from various models against actual observed survival over 48 months.
  • Results showed that while the algorithm systematically identifies models, only certain flexible parametric models accurately predicted long-term survival, with recommendations for minor improvements to enhance its usability.
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Treatment effect waning (TEW) refers to the attenuation of treatment effects over time. Assumptions of a sustained immuno-oncologic treatment effect have been a source of contention in health technology assessment (HTA). We review how TEW has been addressed in HTA and in the wider scientific literature.

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Background: Treatment of advanced or metastatic esophageal adenocarcinoma (EAC) follows the guidelines for gastroesophageal junction adenocarcinoma (GEJC) and gastric adenocarcinoma (GAC), but patients with EAC are often excluded from clinical studies of GEJC/GAC.

Objectives: Here we describe treatment and survival of patients with advanced EAC, GEJC, and GAC to provide population-based evidence on distinctions and similarities between these populations.

Design: Retrospective cohort study of patients with unresectable advanced (cT4b) or metastatic (cM1) EAC, GEJC, or GAC (2015-2020) were selected from the Netherlands Cancer Registry.

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Background: Gastric cancer (GC), gastroesophageal junction cancer (GEJC), and esophageal adenocarcinoma (EAC), together, are leading causes of cancer deaths worldwide. Patient health-related quality of life (HRQoL) and well-being has become increasingly important alongside traditional oncologic outcomes for both patients and clinicians and may aid treatment decisions. We conducted a survey to examine the clinical characteristics, humanistic burden, and the effects of first-line (1L) treatment in patients with GC/GEJC/EAC, across different geographic regions, to address the paucity of real-world data.

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Machine learning has recently been applied and deployed at several light source facilities in the domain of accelerator physics. Here, an approach based on machine learning to produce a fast-executing model is introduced that predicts the polarization and energy of the radiated light produced at an insertion device. This paper demonstrates how a machine learning model can be trained on simulated data and later calibrated to a smaller, limited measured data set, a technique referred to as transfer learning.

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