Evaluation of the Reliability and Validity of the Crawford Classification of Congenital Tibial Dysplasia

2007 ◽  
Author(s):  
David H. Viskochil ◽  
David A. Stevenson ◽  
John C. Carey
Keyword(s):  
Reliability And Validity ◽  
Tibial Dysplasia ◽  
Crawford Classification

scholarly journals Artificial Intelligence in Fractured Dental Implant Detection and Classification: Evaluation Using Dataset from Two Dental Hospitals

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 233
Author(s):  
Dong-Woon Lee ◽  
Sung-Yong Kim ◽  
Seong-Nyum Jeong ◽  
Jae-Hong Lee
Keyword(s):  
Dental Implant ◽  
Classification Accuracy ◽  
Reliability And Validity ◽  
Mechanical Complication ◽  
Radiographic Images ◽  
Acceptable Accuracy ◽  
Reliable Detection ◽  
Classification Evaluation ◽  
Accuracy Performance

Fracture of a dental implant (DI) is a rare mechanical complication that is a critical cause of DI failure and explantation. The purpose of this study was to evaluate the reliability and validity of a three different deep convolutional neural network (DCNN) architectures (VGGNet-19, GoogLeNet Inception-v3, and automated DCNN) for the detection and classification of fractured DI using panoramic and periapical radiographic images. A total of 21,398 DIs were reviewed at two dental hospitals, and 251 intact and 194 fractured DI radiographic images were identified and included as the dataset in this study. All three DCNN architectures achieved a fractured DI detection and classification accuracy of over 0.80 AUC. In particular, automated DCNN architecture using periapical images showed the highest and most reliable detection (AUC = 0.984, 95% CI = 0.900–1.000) and classification (AUC = 0.869, 95% CI = 0.778–0.929) accuracy performance compared to fine-tuned and pre-trained VGGNet-19 and GoogLeNet Inception-v3 architectures. The three DCNN architectures showed acceptable accuracy in the detection and classification of fractured DIs, with the best accuracy performance achieved by the automated DCNN architecture using only periapical images.

scholarly journals ResCaps: an improved capsule network and its application in ultrasonic image classification of thyroid papillary carcinoma

2021 ◽  
Author(s):  
Xiongzhi Ai ◽  
Jiawei Zhuang ◽  
Yonghua Wang ◽  
Pin Wan ◽  
Yu Fu
Keyword(s):  
Papillary Carcinoma ◽  
Network Model ◽  
Reliability And Validity ◽  
Thyroid Papillary Carcinoma ◽  
Data Set ◽  
Ultrasonic Image ◽  
First Choice ◽  
Thyroid Papillary ◽  
Ultrasonic Images

AbstractUltrasonic image examination is the first choice for the diagnosis of thyroid papillary carcinoma. However, there are some problems in the ultrasonic image of thyroid papillary carcinoma, such as poor definition, tissue overlap and low resolution, which make the ultrasonic image difficult to be diagnosed. Capsule network (CapsNet) can effectively address tissue overlap and other problems. This paper investigates a new network model based on capsule network, which is named as ResCaps network. ResCaps network uses residual modules and enhances the abstract expression of the model. The experimental results reveal that the characteristic classification accuracy of ResCaps3 network model for self-made data set of thyroid papillary carcinoma was $$81.06\%$$ 81.06 % . Furthermore, Fashion-MNIST data set is also tested to show the reliability and validity of ResCaps network model. Notably, the ResCaps network model not only improves the accuracy of CapsNet significantly, but also provides an effective method for the classification of lesion characteristics of thyroid papillary carcinoma ultrasonic images.

scholarly journals Reliability and Validity of Classification of Senile Postural Deformity in Mass Examinations

2004 ◽  
Vol 202 (2) ◽  
pp. 105-112 ◽  
Author(s):  
Naoyuki Oi ◽  
Yoshiko Tobimatsu ◽  
Tsutomu Iwaya ◽  
Yasuhiro Okada ◽  
Satoshi Gushiken ◽  
...  
Keyword(s):  
Reliability And Validity ◽  
Postural Deformity

scholarly journals Reliability and validity of ICPC-2-R encoding by medical students

2018 ◽  
Vol 13 (40) ◽  
pp. 1-6
Author(s):  
Leonardo Ferreira Fontenelle ◽  
Álvaro Damiani Zamprogno ◽  
André Filipe Lucchi Rodrigues ◽  
Lorena Camillato Sirtoli ◽  
Natália Josiele Cerqueira Checon ◽  
...  
Keyword(s):  
Primary Care ◽  
Medical Students ◽  
Reliability And Validity ◽  
International Classification ◽  
Free Text ◽  
Reason For Encounter ◽  
Community Physician ◽  
In The Beginning ◽  
Reasons For Encounter

Objective: To estimate how reliably and validly can medical students encode reasons for encounter and diagnoses using the International Classification of Primary Care, revised 2nd edition (ICPC-2-R). Methods: For every encounter they supervised during an entire semester, three family and community physician teachers entered the reasons for encounter and diagnoses in free text into a form. Two of four medical students and one teacher encoded each reason for encounter or diagnosis using the ICPC-2-R. In the beginning of the study, two three-hour workshops were held, until the teachers were confident the students were ready for the encoding. After all the reasons for encounter and the diagnoses had been independently encoded, the seven encoders resolved the definitive codes by consensus. We defined reliability as agreement between students and validity as their agreement with the definitive codes, and used Gwet’s AC1 to estimate this agreement. Results: After exclusion of encounters encoded before the last workshop, the sample consisted of 149 consecutive encounters, comprising 262 reasons for encounter and 226 diagnoses. The encoding had moderate to substantial reliability (AC1, 0.805; 95% CI, 0.767–0.843) and substantial validity (AC1, 0.864; 95% CI, 0.833–0.891). Conclusion: Medical students can encode reasons for encounter and diagnoses with the ICPC-2-R if they are adequately trained.

The Typology of Personality Disorders — Diagnostic Problems and Their Relevance for Suicidal Behavior

Crisis ◽  
1996 ◽  
Vol 17 (2) ◽  
pp. 55-58 ◽  
Author(s):  
Thomas Bronisch
Keyword(s):  
Personality Disorders ◽  
Suicidal Behavior ◽  
Reliability And Validity ◽  
International Classification Of Diseases ◽  
Classification Systems ◽  
Psychological Autopsy ◽  
Diagnostic And Statistical Manual ◽  
Classification Of Diseases ◽  
Methodological Problems

Personality disorders (PD) play an important role in clinical psychiatry. The typologies of personality disorders (PDs) found in different classification systems, such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Classification of Diseases (ICD), are quite congruent. There are many methodological problems with reliability and validity of the diagnosis of PD. However, having a typology seems to be very helpful. Recent psychological autopsy studies reported that about one third of suicide victims met the criteria for a PD. Antisocial PD, borderline PD, narcissistic PD, and depressive PD in particular were often clinically associated with suicidal behavior.

Reliability and validity of a new classification of MIH based on severity

2019 ◽  
Vol 24 (2) ◽  
pp. 727-734 ◽  
Author(s):  
Renata Nunes Cabral ◽  
Bente Nyvad ◽  
Vera Ligia Vieira Mendes Soviero ◽  
Eduardo Freitas ◽  
Soraya Coelho Leal
Keyword(s):  
Reliability And Validity ◽  
New Classification

Validation of an S-O-R Model for Situation, Enduring, and Response Components of Involvement

1982 ◽  
Vol 19 (4) ◽  
pp. 505-516 ◽  
Author(s):  
Raj Arora
Keyword(s):  
Reliability And Validity ◽  
Analysis Approach ◽  
Matrix Approach ◽  
Research Findings ◽  
Linear Structural Relations ◽  
Structural Relations

Despite the importance of the concept of involvement, it remains misunderstood. No attempt has been made to establish the reliability and validity of the concept. To resolve apparently conflicting research findings, Houston and Rothschild posit a paradigm which classifies involvement as situational, enduring, and response. The author assesses the reliability and validity of this tripartite classification of involvement by using a multitrait-multimethod matrix approach and a linear structural relations analysis approach. Subsequently, the S-O-R formulation and causality are also tested.

FUZZY PARTIAL CREDIT SCALING: A VALID APPROACH FOR SCORING THE BECK DEPRESSION INVENTORY

2007 ◽  
Vol 35 (9) ◽  
pp. 1163-1172 ◽  
Author(s):  
Sen-Chi Yu ◽  
Min-Ning Yu
Keyword(s):  
Beck Depression Inventory ◽  
Clinical Sample ◽  
Reliability And Validity ◽  
Psychological Research ◽  
Partial Credit Model ◽  
Partial Credit ◽  
Scaling Method ◽  
Human Thinking ◽  
Latent Traits

In this study a new scaling method was proposed and validated, fuzzy partial credit scaling (FPCS), which combines fuzzy set theory (FST; Zadeh, 1965) with the partial credit model (PCM) for scoring the Beck Depression Inventory (BDI-II; Beck, Steer, & Brown, 1996). To achieve this, the Chinese version of the BDI-II (C-BDI-II) was administered to a clinical sample of outpatients suffering depression, and also to a nonclinical sample. Detailed FPCS procedures were illustrated and the raw score and FPCS were compared in terms of reliability and validity. The Cronbach alpha coefficient showed that the reliability of C-BDI-II was higher in FPCS than in raw score. Moreover, the analytical results showed that, via FPCS, the probability of correct classification of clinical and nonclinical was increased from 73.2% to 80.3%. That is, BDI scoring via FPCS achieves more accurate depression predictions than does raw score. Via FPCS, erroneous judgments regarding depression can be eliminated and medical costs associated with depression can be reduced. This study empirically showed that FST can be applied to psychological research as well as engineering. FST characterizes latent traits or human thinking more accurately than does crisp binary logic.

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