scholarly journals Retrospective analysis of the impact of respiratory motion in treatment margins for frameless lung SBRT based on respiratory‐correlated CBCT data‐sets

2020 ◽  
Vol 21 (10) ◽  
pp. 170-178
Author(s):  
Sheeba Thengumpallil ◽  
Damien Racine ◽  
Jean‐François Germond ◽  
Nicolas Péguret ◽  
Jean Bourhis ◽  
...  
Keyword(s):  
Retrospective Analysis ◽  
Respiratory Motion ◽  
Data Sets ◽  
Lung Sbrt ◽  
Treatment Margins ◽  
The Impact

Sound Production Treatment for Acquired Apraxia of Speech: An Examination of Dosage in Relation to Probe Performance

2020 ◽  
pp. 1-16
Author(s):  
Julie L. Wambaugh ◽  
Lydia Kallhoff ◽  
Christina Nessler
Keyword(s):  
Retrospective Analysis ◽  
Sound Production ◽  
Apraxia Of Speech ◽  
Practice Schedule ◽  
Practice Schedules ◽  
Baseline Performance ◽  
The Mean ◽  
Number Of Treatment ◽  
The Impact

Purpose This study was designed to examine the association of dosage and effects of Sound Production Treatment (SPT) for acquired apraxia of speech. Method Treatment logs and probe data from 20 speakers with apraxia of speech and aphasia were submitted to a retrospective analysis. The number of treatment sessions and teaching episodes was examined relative to (a) change in articulation accuracy above baseline performance, (b) mastery of production, and (c) maintenance. The impact of practice schedule (SPT-Blocked vs. SPT-Random) was also examined. Results The average number of treatment sessions conducted prior to change was 5.4 for SPT-Blocked and 3.9 for SPT-Random. The mean number of teaching episodes preceding change was 334 for SPT-Blocked and 179 for SPT-Random. Mastery occurred within an average of 13.7 sessions (1,252 teaching episodes) and 12.4 sessions (1,082 teaching episodes) for SPT-Blocked and SPT-Random, respectively. Comparisons of dosage metric values across practice schedules did not reveal substantial differences. Significant negative correlations were found between follow-up probe performance and the dosage metrics. Conclusions Only a few treatment sessions were needed to achieve initial positive changes in articulation, with mastery occurring within 12–14 sessions for the majority of participants. Earlier occurrence of change or mastery was associated with better follow-up performance. Supplemental Material https://doi.org/10.23641/asha.12592190

Abstract #701: A Retrospective Analysis of The Impact of Weight Loss on Renal Function

2016 ◽  
Vol 22 ◽  
pp. 145-146
Author(s):  
Tiffany Schwasinger-Schmidt ◽  
Georges Elhomsy ◽  
Fanglong Dong ◽  
Bobbie Paull-Forney
Keyword(s):  
Weight Loss ◽  
Renal Function ◽  
Retrospective Analysis ◽  
The Impact

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

scholarly journals Evaluating the Relationship Between Initial Injury, Referral to A Pain Clinic, and Medical Retirement from the Army: A Retrospective Analysis

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 502-505
Author(s):  
Justin J Stewart ◽  
Diane Flynn ◽  
Alana D Steffen ◽  
Dale Langford ◽  
Honor McQuinn ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Retrospective Analysis ◽  
Extended Period ◽  
Pain Clinic ◽  
Outcome Variable ◽  
Future Research ◽  
Curvilinear Relationship ◽  
The Impact ◽  
Pain Care ◽  
The Relationship

ABSTRACT Introduction Soldiers are expected to deploy worldwide and must be medically ready in order to accomplish their mission. Soldiers unable to deploy for an extended period of time because of chronic pain or other conditions undergo an evaluation for medical retirement. A retrospective analysis of existing longitudinal data from an Interdisciplinary Pain Management Center (IPMC) was used to evaluate the temporal relationship between the time of initial duty restriction and referral for comprehensive pain care to being evaluated for medical retirement. Methods Patients were adults (>18 years old) and were cared for in an IPMC at least once between May 1, 2014 and February 28, 2018. A total of 1,764 patients were included in the final analysis. Logistic regression was used to evaluate the impact of duration between date of first duty restriction documentation and IPMC referral to the outcome variable of establishment of a permanent 3 (P3) profile. Results The duration between date of first duty restriction and IPMC referral showed a curvilinear relationship to probability of a P3 profile. According to our model, a longer duration before referral is associated with an increased probability of a subsequent P3 profile with the highest probability peaking at 19 months. The probability of P3 declines gradually for those who were referred later. Discussion This is the first time the relationship between time of initial duty restriction, referral to an IPMC, and subsequent P3 or higher profile has been tested. Future research is needed to examine medical conditions listed on the profile to see how they might contribute to the cause of referral to the IPMC. Conclusion A longer duration between initial duty restriction and referral to IPMC was associated with higher odds of subsequent P3 status for up to 19 months. Referral to an IPMC for comprehensive pain care early in the course of chronic pain conditions may reduce the likelihood of P3 profile and eventual medical retirement of soldiers.

scholarly journals Diagnosis and treatment delay of head and neck cancers during COVID-19 era in a tertiary care academic hospital: what should we expect?

2021 ◽  
Author(s):  
Pietro De Luca ◽  
Antonella Bisogno ◽  
Vito Colacurcio ◽  
Pasquale Marra ◽  
Claudia Cassandro ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Head And Neck ◽  
Retrospective Analysis ◽  
Tertiary Care ◽  
Diagnostic Delay ◽  
Treatment Delay ◽  
Head And Neck Cancers ◽  
Control Measures ◽  
Academic Hospital ◽  
The Impact

Abstract Background Since the spreading of SARS-CoV-2 from China, all deferrable medical activities have been suspended, to redirect resources for the management of COVID patients. The goal of this retrospective study was to investigate the impact of COVID-19 on head and neck cancers’ diagnosis in our Academic Hospital. Methods A retrospective analysis of patients treated for head and neck cancers between March 12 and November 1, 2020 was carried out, and we compared these data with the diagnoses of the same periods of the 5 previous years. Results 47 patients were included in this study. We observed a significative reduction in comparison with the same period of the previous 5 years. Conclusions Our findings suggest that the COVID-19 pandemic is associated with a decrease in the number of new H&N cancers diagnoses, and a substantial diagnostic delay can be attributable to COVID-19 control measures.

scholarly journals A retrospective analysis of the impact of the COVID-19 restrictions on energy consumption at a disaggregated level

2021 ◽  
Vol 287 ◽  
pp. 116547
Author(s):  
Sebastián García ◽  
Antonio Parejo ◽  
Enrique Personal ◽  
Juan Ignacio Guerrero ◽  
Félix Biscarri ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Retrospective Analysis ◽  
The Impact

Survey Data Quality in Analyzing Harmonized Indicators of Protest Behavior: A Survey Data Recycling Approach

2021 ◽  
pp. 000276422110216
Author(s):  
Kazimierz M. Slomczynski ◽  
Irina Tomescu-Dubrow ◽  
Ilona Wysmulek
Keyword(s):  
Data Processing ◽  
Data Quality ◽  
Survey Data ◽  
A Priori ◽  
Data Sets ◽  
New Approach ◽  
Survey Quality ◽  
Survey Error ◽  
Ex Post ◽  
The Impact

This article proposes a new approach to analyze protest participation measured in surveys of uneven quality. Because single international survey projects cover only a fraction of the world’s nations in specific periods, researchers increasingly turn to ex-post harmonization of different survey data sets not a priori designed as comparable. However, very few scholars systematically examine the impact of the survey data quality on substantive results. We argue that the variation in source data, especially deviations from standards of survey documentation, data processing, and computer files—proposed by methodologists of Total Survey Error, Survey Quality Monitoring, and Fitness for Intended Use—is important for analyzing protest behavior. In particular, we apply the Survey Data Recycling framework to investigate the extent to which indicators of attending demonstrations and signing petitions in 1,184 national survey projects are associated with measures of data quality, controlling for variability in the questionnaire items. We demonstrate that the null hypothesis of no impact of measures of survey quality on indicators of protest participation must be rejected. Measures of survey documentation, data processing, and computer records, taken together, explain over 5% of the intersurvey variance in the proportions of the populations attending demonstrations or signing petitions.

scholarly journals A Two-Stage Joint Model for Nonlinear Longitudinal Response and a Time-to-Event with Application in Transplantation Studies

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Magdalena Murawska ◽  
Dimitris Rizopoulos ◽  
Emmanuel Lesaffre
Keyword(s):  
Empirical Bayes ◽  
Cox Model ◽  
Data Sets ◽  
Two Stage ◽  
Nonlinear Mixed Effects Model ◽  
Resistance Evolution ◽  
Longitudinal Response ◽  
Longitudinal Measurements ◽  
Subject Specific ◽  
The Impact

In transplantation studies, often longitudinal measurements are collected for important markers prior to the actual transplantation. Using only the last available measurement as a baseline covariate in a survival model for the time to graft failure discards the whole longitudinal evolution. We propose a two-stage approach to handle this type of data sets using all available information. At the first stage, we summarize the longitudinal information with nonlinear mixed-effects model, and at the second stage, we include the Empirical Bayes estimates of the subject-specific parameters as predictors in the Cox model for the time to allograft failure. To take into account that the estimated subject-specific parameters are included in the model, we use a Monte Carlo approach and sample from the posterior distribution of the random effects given the observed data. Our proposal is exemplified on a study of the impact of renal resistance evolution on the graft survival.

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