RESUMO
Studies of human movement usually collect data from multiple repetitions of a task and use the average of all movement trials to approximate the typical kinematics or kinetics pattern for each individual. Few studies report the expected accuracy of these estimated mean kinematics or kinetics waveforms for each individual. The purpose of this study is to demonstrate how simultaneous confidence bands, which is an approach to quantify uncertainty across an entire waveform based on limited data, can be used to calculate margin of error (MOE) for waveforms. Bilateral plantar pressure data were collected from 70 participants as they walked over 4 surfaces for an average of at least 300 steps per surface. The relationship between MOE and the number of steps included in the analysis was calculated using simultaneous confidence bands, and 3 methods commonly used for pointwise estimates: intraclass correlation, sequential averaging, and T-based MOE. The conventional pointwise approaches underestimated the number of trials required to estimate biomechanical waveforms within a desired MOE. Simultaneous confidence bands are an objective approach to more accurately estimate the relationship between the number of trials collected and the MOE in estimating typical biomechanical waveforms.
Assuntos
Movimento , Caminhada , Fenômenos Biomecânicos , Humanos , Reprodutibilidade dos TestesRESUMO
Background: Magnetic resonance (MR) scans are routine clinical procedures for monitoring people with multiple sclerosis (PwMS). Patient discomfort, timely scheduling, and financial burden motivate the need to accelerate MR scan time. We examined the clinical application of a deep learning (DL) model in restoring the image quality of accelerated routine clinical brain MR scans for PwMS. Methods: We acquired fast 3D T1w BRAVO and fast 3D T2w FLAIR MRI sequences (half the phase encodes and half the number of slices) in parallel to conventional parameters. Using a subset of the scans, we trained a DL model to generate images from fast scans with quality similar to the conventional scans and then applied the model to the remaining scans. We calculated clinically relevant T1w volumetrics (normalized whole brain, thalamic, gray matter, and white matter volume) for all scans and T2 lesion volume in a sub-analysis. We performed paired t-tests comparing conventional, fast, and fast with DL for these volumetrics, and fit repeated measures mixed-effects models to test for differences in correlations between volumetrics and clinically relevant patient-reported outcomes (PRO). Results: We found statistically significant but small differences between conventional and fast scans with DL for all T1w volumetrics. There was no difference in the extent to which the key T1w volumetrics correlated with clinically relevant PROs of MS symptom burden and neurological disability. Conclusion: A deep learning model that improves the image quality of the accelerated routine clinical brain MR scans has the potential to inform clinically relevant outcomes in MS.
RESUMO
BACKGROUND AND OBJECTIVES: Ampullary adenocarcinoma (AA) is classified by immunohistochemical (IHC) subtypes into intestinal (IN), pancreatobiliary (PB), and ambiguous (AM). The impact of adjuvant therapy on IHC subtype and disease stage is unclear. We examined the effect of adjuvant chemotherapy regimen on survival of ampullary cancers by IHC subtype and disease stage. METHODS: Review of pancreatoduodenectomy (PD) performed for AA between 2005 and 2013 at a single center. The impact of regimen on IHC subtype and stage was analyzed. RESULTS: One hundred and twenty-one patients were subtyped: IN = 32%, PB = 48%, and AM = 20% with overall survival of 45.6, 31.3, and 46.9 months, respectively. PB had higher pathologic T-stage, positive lymph node disease, and perineural and lymphovascular invasion (P < .05). 5-Fluorouracil (FU)-based adjuvant therapy improved survival compared to no treatment (87.4 vs 32.1 months; P = .046), and receipt of 5-FU emerged as an independent predictor of improved survival (hazard ratio [HR] 0.244; P = .031) regardless of subtype. 5-FU was superior to Gemcitabine in advanced-stage disease (stage IIB and III vs I+IIA, HR: 0.35; P < .05). CONCLUSIONS: Adjuvant therapy with 5-FU confers a survival benefit in patients with advanced-stage AA regardless of subtype. The impact of various chemotherapy regimens on subtypes of ampullary cancer warrants further investigation.
