Background: Modifying the consistency of food and drink is a strategy commonly used in the management of dysphagia for people with intellectual disabilities (ID). People with ID often depend on others for the preparation of food and drink and therefore depend on those caregivers achieving the correct consistency to keep them safe and avoid discomfort during mealtimes. Clinical experience and prior research have demonstrated that although training can improve modification, carers often find modification difficult and potentially stressful and recommend additional support for carers. Fluid consistency is often modified through the addition of powdered thickener. This study investigates the efficacy of typical training and use of consistency guides, the Thickness Indicator Model (TIM) tubes, in helping carers to modify fluids accurately.
Method: A 3 × 3 pre-post experimental design with a control group was employed to compare the observed accuracy of modification across three groups and at three time points (pre-intervention baseline, immediately post-training intervention and 3-10 months post-training). Sixty-two paid carers who supported people with ID were recruited to participate in the study and each was randomly allocated to one of the three groups: a control group given written guidance only, a group who received typical training and written guidance and a group who received training, written guidance and the TIM tubes.
Results & Conclusions: Typical training resulted in significantly greater carer accuracy in modifying fluid consistencies when compared with written guidance alone. Use of the TIM tubes also significantly improved accuracy in the modification of drinks compared with the group who modified with the aid of written guidance alone. At 3-10-month follow-up only the group who received typical training alongside the TIM tubes were significantly more accurate than the Written Guidance group. Further research is warranted to ascertain the effectiveness of the training and the utility of the TIM tubes in improving accuracy over a longer time scale and in individuals' usual living environments.
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http://dx.doi.org/10.1111/jir.12013 | DOI Listing |
Int J Comput Assist Radiol Surg
March 2025
Ircad Africa, Kigali, Rwanda.
Purpose: Despite major advances in Computer Assisted Diagnosis (CAD), the need for carefully labeled training data remains an important clinical translation barrier. This work aims to overcome this barrier for ultrasound video-based CAD, using video-level classification labels combined with a novel training strategy to improve the generalization performance of state-of-the-art (SOTA) video classifiers.
Methods: SOTA video classifiers were trained and evaluated on a novel ultrasound video dataset of liver and kidney pathologies, and they all struggled to generalize, especially for kidney pathologies.
J Med Internet Res
March 2025
GSK, London, United Kingdom.
Background: Uncomplicated urinary tract infections (uUTIs) affect more than half of women in their lifetime and can impact on quality of life. We analyzed social media posts discussing uUTIs to gather insights into the patient experience, including aspects of their disease management journey and associated opinions and concerns.
Objective: This study aims to gather patient experience insights by analyzing social media posts that discussed uUTI.
BMC Res Notes
March 2025
Department of Medical Education, Medical Education Research Center, Fasa University of Medical Sciences, Fasa, Iran.
Objective: Patient education at the time of discharge using models which aim to improve self-care behaviors can significantly contribute to patients' adoption of a healthy lifestyle and treatment adherence. This is a randomized controlled clinical trial with no blinding in which we tested two groups of intervention control. 90 patients having undergone coronary angioplasty were allocated to an intervention (N = 45) and a control group randomly (N = 45).
View Article and Find Full Text PDFBMC Med Inform Decis Mak
March 2025
Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Purpose: Assessing risk factors and creating prediction models from real-world medical data is challenging, requiring numerous modelling decisions with clinical guidance. Logistic regression is a common model for such studies, for which we advocate the use of Bayesian methods that can jointly deliver probabilistic risk factor inference and prediction. As an exemplar, we compare Bayesian logistic regression with horseshoe priors and Projective Prediction variable selection with the established frequentist LASSO approach, to predict severe COVID-19 outcomes (death or ICU admittance) from demographic and laboratory biomarker data.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
March 2025
Institute for Public Health and Nursing Research, University of Bremen, Bremen, Germany.
Background: Many patients with cancer want to be involved in healthcare decisions. For adequate participation, awareness of one's own desires and preferences and sufficient knowledge about medical measures are indispensable. In order to support patient participation, a decision guide for patients with cancer was developed as part of a larger project called TARGET, which specifically aims to improve the care of patients with rare cancer.
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