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When one initially targets the null effect and the point estimate falls close to the null, two challenges exist in rating certainty of evidence. First, when the point estimate is near the null and the data, therefore, suggests little or no effect, rating certainty in a benefit or harm is misleading. Second, since in general the narrower the confidence interval (CI) the more precise the estimate, if the CI is narrow, rating down for imprecision due simply to crossing the null is inappropriate. This paper addresses these issues and provides a solution: to revise the target of certainty rating from a non-zero effect to a little or no effect. This solution requires estimating a range in which the minimal important difference (MID) for benefit and an MID for harm might lie, and thus establishing a range that represents little or no effect. If GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) users are confident that the point estimate represents an effect less than the smallest plausible MID, they will revise their target and rate certainty to a little or no effect. If the entire CI falls within the range of little or no effect, they will not rate down for imprecision. Otherwise (if the CI includes an important effect), they will rate down. Using the solution provided in this paper GRADE users can make an optimal choice of the target of certainty rating.
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http://dx.doi.org/10.1136/bmjebm-2024-113077 | DOI Listing |
J Mass Spectrom
October 2025
Department of Chemistry and Technology of Drugs, "Sapienza" University of Rome, Rome, Italy.
Ionic liquids (ILs) are a class of organic salts with melting points below 100°C. Owing to their unique chemical and physical properties, they are used as solvents and catalysts in various chemical transformations, progressively replacing common volatile organic solvents (VOCs) in green synthetic applications. However, their intrinsic ionic nature can restrict the use of mass spectrometric techniques to monitor the time progress of a reaction occurring in an IL medium, thus preventing one from following the formation of the reaction products or intercepting the reaction intermediates.
View Article and Find Full Text PDFRadiol Adv
September 2024
Department of Radiology, Northwestern University and Northwestern Medicine, Chicago, IL, 60611, United States.
Background: In clinical practice, digital subtraction angiography (DSA) often suffers from misregistration artifact resulting from voluntary, respiratory, and cardiac motion during acquisition. Most prior efforts to register the background DSA mask to subsequent postcontrast images rely on key point registration using iterative optimization, which has limited real-time application.
Purpose: Leveraging state-of-the-art, unsupervised deep learning, we aim to develop a fast, deformable registration model to substantially reduce DSA misregistration in craniocervical angiography without compromising spatial resolution or introducing new artifacts.
Clin Kidney J
September 2025
Department of Nephrology, CHU Lyon, Lyon, France.
Background: Patients receiving haemodialysis (HD) experience symptoms that impact quality of life. This study assessed the concordance of symptoms and symptom severity of HD patients and their perception by nurses and nephrologists.
Methods: A cross-sectional, observational study using the 30-item Dialysis Symptom Index (DSI) questionnaire was conducted in six dialysis centres in France from 1 March 2022 to 30 June 2023.
Clin Kidney J
September 2025
Department of Nephrology and Institute of Nephrology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Background: This study aimed to evaluate the efficacy and safety of telitacicept versus mycophenolate mofetil (MMF) in high-risk progressive immunoglobulin A nephropathy (IgAN).
Methods: This retrospective, multicentre cohort study included patients with high-risk progressive IgAN who received telitacicept or MMF therapy, both combined with low-dose steroids. Clinical data were collected from treatment initiation to 12 months.
J Appl Stat
January 2025
Department of Sociology, University of Pennsylvania, Philadelphia, PA, USA.
This paper presents a causal inference estimation method for longitudinal observational studies with multiple outcomes. The method uses marginal structural models with inverse probability treatment weights (MSM-IPTWs). In developing the proposed method, we re-define the weights as a product of inverse weights at each time point, accounting for time-varying confounders and treatment exposures and possible correlation between and within (serial) the multiple outcomes.
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