ABSTRACT
OBJECTIVE: This review will evaluate the effectiveness of dose-intensified versus standard-dose salvage regimens on progression-free survival in early progressed follicular lymphoma before high-dose chemotherapy and autologous stem cell transplantation. INTRODUCTION: Despite the substantial advances in the management of follicular lymphoma, approximately 20% of patients experience progression of the disease within 2 years of induction therapy. These patients have worse outcomes, and autologous stem cell transplantation is deemed to improve outcomes in this context. Little is known about the optimal salvage regimen. INCLUSION CRITERIA: Studies must include patients ≥18 years old with early progressed follicular lymphoma who were submitted to autologous stem cell transplantation in subsequent remission. Clinical trials and observational studies will be included. METHODS: The search strategy will be carried out in MEDLINE (PubMed), Embase (Periódicos CAPES), Scopus, Web of Science, LiLACS, and the Cochrane Library. No date or language restrictions will be imposed. The recommended JBI approach to critical appraisal, study selection, data extraction, and data synthesis will be used. Studies should score at least 50% in accordance with the critical appraisal tool. Data will be pooled whenever possible using the random effects model. Heterogeneity will be assessed using the standard χ2 and I2 tests. A funnel plot will be generated to assess publication bias if there are 10 or more studies included in the meta-analysis. The GRADE approach will be used to rate certainty of evidence. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO CRD42022373345.
ABSTRACT
Objective: Although clinical decision support systems (CDSS) have many benefits for clinical practice, they also have several barriers to their acceptance by professionals. Our objective in this study was to design and validate The Aleph palliative care (PC) CDSS through a user-centred method, considering the predictions of the artificial intelligence (AI) core, usability and user experience (UX). Methods: We performed two rounds of individual evaluation sessions with potential users. Each session included a model evaluation, a task test and a usability and UX assessment. Results: The machine learning (ML) predictive models outperformed the participants in the three predictive tasks. System Usability Scale (SUS) reported 62.7 ± 14.1 and 65 ± 26.2 on a 100-point rating scale for both rounds, respectively, while User Experience Questionnaire - Short Version (UEQ-S) scores were 1.42 and 1.5 on the -3 to 3 scale. Conclusions: The think-aloud method and including the UX dimension helped us to identify most of the workflow implementation issues. The system has good UX hedonic qualities; participants were interested in the tool and responded positively to it. Performance regarding usability was modest but acceptable.
