Background: Assessing the quality of clinical research is a key evidence-based practice skill. Clinicians, guideline producers, policy makers, service commissioners, and families need to have a sense of the validity, applicability, and certainty of research evidence when determining how it should inform their decision-making and practice.
Methods: We consider the various methodological and study design factors that contribute to the validity and applicability of clinical research findings. We describe the "Grading of Recommendations Assessment, Development and Evaluation" (GRADE) methodology and discuss how this approach is used to assess and report certainty of evidence and strength of recommendations.
Results: The randomized controlled trial (RCT) is the gold standard method for assessing interventions because randomization balances prognostic characteristics between comparison groups. The GRADE approach considers evidence from RCTs as high quality, but acknowledges that the quality and level of certainty of trial evidence may be "downgraded" based on consideration of threats across 5 domains: risk of bias in included trials, inconsistency between trials in outcome estimates, indirectness of the evidence, imprecision of estimates, and likelihood of publication bias.
Conclusions: Structured critical appraisal using GRADE methods to assess risk of bias and other threats to the internal and external validity of RCTs and systematic reviews and meta-analyses of their data facilitates transparency and consistency in using evidence to inform policy and practice.
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http://dx.doi.org/10.1159/000516239 | DOI Listing |
JAMA Netw Open
January 2025
Division of Geriatrics, School of Medicine, University of California San Francisco.
Importance: The Walter Index is a widely used prognostic tool for assessing 12-month mortality risk among hospitalized older adults. Developed in the US in 2001, its accuracy in contemporary non-US contexts is unclear.
Objective: To evaluate the external validity of the Walter Index in predicting posthospitalization mortality risk in Brazilian older adult inpatients.
JAMA Psychiatry
January 2025
Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.
Importance: Depressive symptoms are associated with cognitive decline in older individuals. Uncertainty about underlying mechanisms hampers diagnostic and therapeutic efforts. This large-scale study aimed to elucidate the association between depressive symptoms and amyloid pathology.
View Article and Find Full Text PDFMed Biol Eng Comput
January 2025
Anhui BioX-Vision Biological Technology Co., Ltd, Hefei, 230031, Anhui, China.
The identification and categorization of circulating tumor cells (CTCs) in peripheral blood are imperative for advancing cancer diagnostics and prognostics. The intricacy of various CTCs subtypes, coupled with the difficulty in developing exhaustive datasets, has impeded progress in this specialized domain. To date, no methods have been dedicated exclusively to overcoming the classification challenges of CTCs.
View Article and Find Full Text PDFAbdom Radiol (NY)
January 2025
The General Hospital of Western Theater Command, Chengdu, Sichuan 610083, China, Chengdu, China.
Background: Perineural invasion (PNI) in colorectal cancer (CRC) is a significant prognostic factor associated with poor outcomes. Radiomics, which involves extracting quantitative features from medical imaging, has emerged as a potential tool for predicting PNI. This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of radiomics models in predicting PNI in CRC.
View Article and Find Full Text PDFObjectives: Combining Computed Tomography (CT) intuitive anatomical features with Three-Dimensional (3D) CT multimodal radiomic imaging features to construct a model for assessing the aggressiveness of pancreatic neuroendocrine tumors (pNETs) prior to surgery.
Methods: This study involved 242 patients, randomly assigned to training (170) and validation (72) cohorts. Preoperative CT and 3D CT radiomic features were used to develop a model predicting pNETs aggressiveness.
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