• 제목/요약/키워드: retest cost

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정신훈련이 뇌졸중환자의 상지 기능회복에 미치는 영향 (The effect of Mental Practice on the upper extremity function of the Hemiplegic Patients)

  • 박창식;안승헌
    • 대한물리치료과학회지
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    • 제12권3호
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    • pp.59-69
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    • 2005
  • The purpose of this study was to investigate the effect of mental practice on the upper extremity function of the hemiplegic Patients, 8 subjects with hemiplegia patients participated in this study. They did the Mental Practice programme for twenty minute, two times a week, three weeks. The Mental practice effects was evaluated by Fugl-Meyer Assessment(FMA), Action Research Arm Test(ARA), Stroke Rehabilitation Assessment of Movement(STREAM). The data were analyzed using paired t-test. The result of this study are as follows: FMA score were significant difference test-retest(p<.05), ARA score were statistically significant improvement between test-retest(p<.05), On the STREAM, the patients had the score improved on 4 of the 10 items of the upper-extremity Scale. The result suggest that mental practice is a potentially useful method of practicing motor skills. Mental practice may be a cost-effective, non-invasive tool with wuhich patients with stroke can receive additional practice of functional skills, and realize greater outcome, than if therapy alone were used.

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객체 지향 시스템에서의 클래스 간 의존성 강도 측정을 위한 커플링 척도 (A Coupling Metric for Measuring Strength of Dependency between Classes in Object-Oriented Systems)

  • 화지민;이숙희;권용래
    • 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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    • 제14권1호
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    • pp.81-85
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    • 2008
  • 객체지향 패러다임에서 커플링은 유지보수활동에 가장 큰 영향을 주는 속성 중 하나로 많은 연구들이 진행되어 왔다. 그러나 기존의 커플링 척도는 클래스들간의 의존성 여부만 측정할 뿐 의존성 강도를 표현하지 못하기 때문에 리팩토링이나 시스템 분해와 같이 의존성 강도 정보가 필요한 유지보수활동에서 의사 결정을 지원하는데 한계가 있다. 이 논문에서는 이를 해결하기 위해 클래스 간의 의존성 여부뿐만 아니라 유지 보수성-재검사 비용 측면에서 클래스 간의 의존성 강도까지 표현할 수 있는 커플링 척도를 제안한다. 그리고 시스템 분해 문제에 적용한 예제를 통하여 시스템 유지 보수를 위한 여러 분야에 적용될 수 있음을 보인다.

Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation

  • Chae Jung Park;Yae Won Park;Sung Soo Ahn;Dain Kim;Eui Hyun Kim;Seok-Gu Kang;Jong Hee Chang;Se Hoon Kim;Seung-Koo Lee
    • Korean Journal of Radiology
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    • 제23권1호
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    • pp.77-88
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    • 2022
  • Objective: Our study aimed to evaluate the quality of radiomics studies on brain metastases based on the radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guidelines. Materials and Methods: PubMed MEDLINE, and EMBASE were searched for articles on radiomics for evaluating brain metastases, published until February 2021. Of the 572 articles, 29 relevant original research articles were included and evaluated according to the RQS, TRIPOD checklist, and IBSI guidelines. Results: External validation was performed in only three studies (10.3%). The median RQS was 3.0 (range, -6 to 12), with a low basic adherence rate of 50.0%. The adherence rate was low in comparison to the "gold standard" (10.3%), stating the potential clinical utility (10.3%), performing the cut-off analysis (3.4%), reporting calibration statistics (6.9%), and providing open science and data (3.4%). None of the studies involved test-retest or phantom studies, prospective studies, or cost-effectiveness analyses. The overall rate of adherence to the TRIPOD checklist was 60.3% and low for reporting title (3.4%), blind assessment of outcome (0%), description of the handling of missing data (0%), and presentation of the full prediction model (0%). The majority of studies lacked pre-processing steps, with bias-field correction, isovoxel resampling, skull stripping, and gray-level discretization performed in only six (20.7%), nine (31.0%), four (3.8%), and four (13.8%) studies, respectively. Conclusion: The overall scientific and reporting quality of radiomics studies on brain metastases published during the study period was insufficient. Radiomics studies should adhere to the RQS, TRIPOD, and IBSI guidelines to facilitate the translation of radiomics into the clinical field.

Quality Reporting of Radiomics Analysis in Mild Cognitive Impairment and Alzheimer's Disease: A Roadmap for Moving Forward

  • So Yeon Won;Yae Won Park;Mina Park;Sung Soo Ahn;Jinna Kim;Seung-Koo Lee
    • Korean Journal of Radiology
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    • 제21권12호
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    • pp.1345-1354
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    • 2020
  • Objective: To evaluate radiomics analysis in studies on mild cognitive impairment (MCI) and Alzheimer's disease (AD) using a radiomics quality score (RQS) system to establish a roadmap for further improvement in clinical use. Materials and Methods: PubMed MEDLINE and EMBASE were searched using the terms 'cognitive impairment' or 'Alzheimer' or 'dementia' and 'radiomic' or 'texture' or 'radiogenomic' for articles published until March 2020. From 258 articles, 26 relevant original research articles were selected. Two neuroradiologists assessed the quality of the methodology according to the RQS. Adherence rates for the following six key domains were evaluated: image protocol and reproducibility, feature reduction and validation, biologic/clinical utility, performance index, high level of evidence, and open science. Results: The hippocampus was the most frequently analyzed (46.2%) anatomical structure. Of the 26 studies, 16 (61.5%) used an open source database (14 from Alzheimer's Disease Neuroimaging Initiative and 2 from Open Access Series of Imaging Studies). The mean RQS was 3.6 out of 36 (9.9%), and the basic adherence rate was 27.6%. Only one study (3.8%) performed external validation. The adherence rate was relatively high for reporting the imaging protocol (96.2%), multiple segmentation (76.9%), discrimination statistics (69.2%), and open science and data (65.4%) but low for conducting test-retest analysis (7.7%) and biologic correlation (3.8%). None of the studies stated potential clinical utility, conducted a phantom study, performed cut-off analysis or calibration statistics, was a prospective study, or conducted cost-effectiveness analysis, resulting in a low level of evidence. Conclusion: The quality of radiomics reporting in MCI and AD studies is suboptimal. Validation is necessary using external dataset, and improvements need to be made to feature reproducibility, feature selection, clinical utility, model performance index, and pursuits of a higher level of evidence.