scholarly journals Intraobserver and interobserver reliability of the computed tomography-based radiographic classification of primary elbow osteoarthritis: comparison with plain radiograph-based classification and clinical assessment

2019 ◽  
Vol 27 (7) ◽  
pp. 1057-1063 ◽  
Author(s):  
J.-M. Kwak ◽  
E. Kholinne ◽  
Y. Sun ◽  
A.M. Alhazmi ◽  
K.-H. Koh ◽  
...  
Author(s):  
Chaintiou Piorno Romina ◽  
Consoli Lizzi Eugenia Pilar ◽  
Saiegh Jonathan ◽  
Vázquez Diego Jorge ◽  
Gualtieri Ariel Félix ◽  
...  

Introduction:To evaluate cone-beam computed tomography (CBCT) images in order to determine the presence of mandibular second molars with C-shaped canal system and classify them.Methods:3035 CBCT images fulfilling the selection criteria were observed. Once established the presence of C-shaped canal system, they were classified according to the anatomic and radiographic classification of Fan et al. Data description was made by frequencies and percentages rates, with a 95% confidence interval (IC95) according to score method. Comparisons were assessed by means of the Chi-square test with a significance level equal to 5%.Results:Of the 225 selected patients, 44 exhibited C-shaped canals (20%; IC95: 15% to 25%). 70% (IC95: 56% to 82%) of patients showed a bilateral C-shaped canal system pattern. Regarding to the axial plane -anatomic classification-, there was a significant association between the root third and the configuration (Chi-square=76.89; p<0.05): at the coronal third prevailed the C1 configuration (47%; IC95: 36% to 58%); at the middle third prevailed the C3d configuration (39%; IC95: 28% to 50%) and at the apical third, the C4 configuration (35%; IC95: 25% to 46%).


2013 ◽  
Vol 6;16 (6;11) ◽  
pp. 569-580
Author(s):  
Hans Timmerman

Background: Neuropathic pain (NeP) is a burdensome problem in all stages of cancer. Although clinical judgment is accepted as a surrogate for an objective gold standard in diagnosing NeP, no publications were found about its reliability. Objectives: Therefore, levels of agreement on the clinical examination of NeP were estimated by calculating kappa-value (Κ) and percentage of pair wise agreement (PA) to determine the interobserver reliability of diagnosing NeP. Setting: The outpatient clinic of medical oncology of the Radboud University Nijmegen Medical Centre. Methods: Patients with cancer with potential NeP complaints were recruited from the outpatient clinic of medical oncology. Physicians were recruited from the department of pain and palliative medicine. Physicians and patients were recruited for participation in an observational study in daily practice. Each patient (N = 34) was examined by 2 specialists via independent clinical assessment. All consultations were video recorded. After each assessment, physicians were asked to indicate the most adequate characterization of the pain: pure NeP, pure nociceptive pain (NoP), mixed pain (MiP), or no pain (NP). Results: Kappa (Κ) for the diagnosis of the most adequate pain characterization was 0.50, PA 64.7%. For diagnosing pure NeP k was 0.78 (PA 91.2%), for the NeP component (NeP + MiP) and NoP component (NoP + MiP), it was respectively 0.52 (PA 76.5%) and 0.61 (PA 82.4%). For the diagnosis on the basis of the grading system between physicians, Κ was 0.34 (PA 52.9%). The intrarater reliability for the diagnosis of an NeP component on the basis of clinical assessment and the NeP component on the basis of the grading system, for pain specialists Κ was 0.69 (PA 85.3%) and for palliative care specialists Κ was 0.61 (PA 79.4%). Limitations: The values of Κ and the PA for the existence of an NeP component are not satisfying and the clinical agreement between physicians around findings from physical examination should encourage a better standardization of the clinical assessment and classification of pain in patients with cancer in respect with the identification of NeP. Conclusions: A substantial level of agreement was found for the diagnosis of pure NeP and a moderate level of agreement for the diagnosis of the NeP component was found, both with a PA ≥ 70%. There was only a fair agreement between the physicians regarding the grading system. However, there was a substantial level of (interrater) agreement for the diagnosis of an NeP component and the outcome of the grading system. The findings in this study also suggest that a better standardization of the clinical assessment and classification of pain in patients with cancer with respect to the identification of neuropathic pain is necessary. Key words: Neuropathic pain, diagnosis, interobserver reliability, agreement, cancer observational study, pain, clinical assessment, diagnostic test


Skull Base ◽  
2007 ◽  
Vol 16 (S 2) ◽  
Author(s):  
Su-Jin Han ◽  
Sang-Woo Moon ◽  
Mee-Hyun Song ◽  
Ho-Ki Lee

1999 ◽  
Vol 28 (8) ◽  
pp. 662-681 ◽  
Author(s):  
M. Blauth ◽  
◽  
L. Bastian ◽  
C. Knop ◽  
U. Lange ◽  
...  

2021 ◽  
Author(s):  
A. V. Vodovatov ◽  
S. A. Ryzhov ◽  
L. A. Chipiga ◽  
A. M. Biblin ◽  
P. S. Druzhinina

Cancer ◽  
2011 ◽  
Vol 118 (14) ◽  
pp. 3501-3511 ◽  
Author(s):  
Rodrigo Oliva Perez ◽  
Angelita Habr-Gama ◽  
Joaquim Gama-Rodrigues ◽  
Igor Proscurshim ◽  
Guilherme Pagin São Julião ◽  
...  

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 893
Author(s):  
Yazan Qiblawey ◽  
Anas Tahir ◽  
Muhammad E. H. Chowdhury ◽  
Amith Khandakar ◽  
Serkan Kiranyaz ◽  
...  

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder–Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively.


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