The First-year Integration Test: a validation study
Ontology highlight
ABSTRACT:
SUBMITTER: Willems J
PROVIDER: S-EPMC10448899 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Background and purposeAbnormal postural sway is associated with an increase in risk of falls but is difficult for clinicians to accurately quantify without access to laboratory equipment. Instrumenting clinical outcome measures using body-worn movement monitors is a low-cost alternative. This is the first study to compare the modified Clinical Test of Sensory Integration for Balance (i-mCTSIB) to the laboratory test of the Sensory Organization Test (SOT) with dynamic posturography in a group of participants with Parkinson's disease (PD) and subtle balance limitations. The purpose of this study was to (1) determine the concurrent validity of the i-mCTSIB with the SOT (6 and 4 conditions) and (2) compare the i-mCTSIB and the SOT to differentiate between individuals with and without recent falls within the previous 6 months.MethodsThis cross-sectional study examined 26 participants with idiopathic PD who had a Motor Unified Parkinson's Disease Rating Scale score of 32.7 (13.5) out of 108.ResultsThe composite and conditions 1 and 4 of the i-mCTSIB and SOT scores were significantly correlated: composite scores r = -0.64 (P ≤ .001), C1 r = -0.43 (P = .03), C3 r = -0.60 (P ≤ .01), and C4 r = -0.54 (P ≤ .001). A significant difference was observed in mean i-mCTSIB composite scores between fallers and nonfallers (P = .04). In contrast, the SOT composite was not significantly different between fallers and nonfallers (P = 0.31).DiscussionThe results suggest that the i-mCTSIB may be a valid and clinically meaningful measure of sensory organization in persons with PD, even those with mild postural instability as measured by the median Hoehn and Yahr score (2.0). Future research should evaluate predictive validity of the i-mCTSIB for prospective falls.ConclusionThe instrumented mCTSIB with portable, body-worn movement allows clinicians to quantify abnormal postural sway without the ceiling effects of clinical balance testing or the expense and importability of force plate technology in the SOT. Instrumenting mCTSIB may also distinguish between fallers and nonfallers.
| S-EPMC5441972 | biostudies-literature
Project description:The Fisher College of Science and Mathematics (FCSM) at Towson University (TU) has integrated authentic research experiences throughout the curriculum from first year STEM courses through advanced upper-level classes and independent research. Our observation is that training in both responsible conduct of research (RCR) and bioethics throughout the curriculum was an effective strategy to advance the cognitive and psychosocial development of the students. As students enter TU they generally lack the experience and tools to assess their own competence, to apply ethical debates, to investigate scientific topics from an ethical perspective, or to integrate ethics into final conclusions. Student behavior and development follow cognitive models such as described in the theories put forth by Piaget, Kohlberg, and Erikson, both for initial learning and for how concepts are understood and adopted. Three examples of this ethics training integration are described, including a cohort-based course for first year students in the STEM Residential Learning Community, a cohort-based course for community college students that are involved in an NIH-funded Bridges to the Baccalaureate program, and a senior seminar in Bioethics in the Molecular Biology, Biochemistry and Bioinformatics Program. All three focus on different aspects of RCR and bioethics training, providing opportunities for students to learn about the principles of effective decision-making, critical and analytical thinking, problem solving, and communication with increasing degrees of complexity as they move through the curriculum.
| S-EPMC4278481 | biostudies-literature
Project description:BackgroundThe first-year survival rate among patients undergoing hemodialysis remains poor. Current mortality risk scores for patients undergoing hemodialysis employ regression techniques and have limited applicability and robustness.ObjectiveWe aimed to develop a machine learning model utilizing clinical factors to predict first-year mortality in patients undergoing hemodialysis that could assist physicians in classifying high-risk patients.MethodsTraining and testing cohorts consisted of 5351 patients from a single center and 5828 patients from 97 renal centers undergoing hemodialysis (incident only). The outcome was all-cause mortality during the first year of dialysis. Extreme gradient boosting was used for algorithm training and validation. Two models were established based on the data obtained at dialysis initiation (model 1) and data 0-3 months after dialysis initiation (model 2), and 10-fold cross-validation was applied to each model. The area under the curve (AUC), sensitivity (recall), specificity, precision, balanced accuracy, and F1 score were used to assess the predictive ability of the models.ResultsIn the training and testing cohorts, 585 (10.93%) and 764 (13.11%) patients, respectively, died during the first-year follow-up. Of 42 candidate features, the 15 most important features were selected. The performance of model 1 (AUC 0.83, 95% CI 0.78-0.84) was similar to that of model 2 (AUC 0.85, 95% CI 0.81-0.86).ConclusionsWe developed and validated 2 machine learning models to predict first-year mortality in patients undergoing hemodialysis. Both models could be used to stratify high-risk patients at the early stages of dialysis.
| S-EPMC7661257 | biostudies-literature
Project description:Metatranscriptomes obtained from AFISsys prototype 1, AFIS and free-flow bottles, over 24 hours
| PRJEB29369 | ENA
Project description:A sample-to-report solution for taxonomic identification of cultured bacteria in the clinical setting based on nanopore sequencing
| PRJEB34167 | ENA