Electronic Assessment of Quality of Life in Glioma Patients Using the EORTC-QLQ-C30/ EORTC-BN20 and the PROMIS System in Clinical Practice (P3-6.008)

We aim to assess feasibility of computer adaptations of the Patient-Reported Outcomes Measurement Information System (PROMIS) questionnaire in the clinical setting and compare it with outcomes of the EORTC-QLQ-C30 and EORTC-BN20 questionnaires.

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Publication Details

Journal
Neurology
Published
2025-04-07
DOI
https://doi.org/10.1212/wnl.0000000000212690
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

Electronic Assessment of Quality of Life in Glioma Patients Using the EORTC-QLQ-C30/ EORTC-BN20 and the PROMIS System in Clinical Practice (P3-6.008)

Tobias Walbert, D Mohn, Lonni Schultz, James M. Snyder
Neurology
Radiomics and Machine Learning in Medical Imaging
article

Electronic Assessment of Quality of Life in Glioma Patients Using the EORTC-QLQ-C30/ EORTC-BN20 and the PROMIS System in Clinical Practice (P3-6.008)

Tobias Walbert, D Mohn, Lonni Schultz, James M. Snyder
article en

Abstract

We aim to assess feasibility of computer adaptations of the Patient-Reported Outcomes Measurement Information System (PROMIS) questionnaire in the clinical setting and compare it with outcomes of the EORTC-QLQ-C30 and EORTC-BN20 questionnaires.

NeurologyVol. 104(7_Supplement_1)
Henry Ford Health System (US), Henry Ford Hospital (US), Neurology, Inc (US)
Openalex Percentile: Top 89%
Radiomics and Machine Learning in Medical Imaging
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