• Journal of Sasang Constitutional Medicine
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• v.15 no.3
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• pp.74-87
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• 2003

Purpose This study checked the present conditions of looking into self-checking questionnaires for indentifing Sasangin. And then it will give key knowledges of developing new questionnaires. Method The 22 papers reported in journals from 1986 to 2003 were analysised on each purpose, method, subjects, and results. Result On questionnaires for indentifing Sasangin(I) have been written a paper of reliability, a paper of validation, and a paper of comparison of responses. On QSCC have been written 2 papers of validation, and 2 papers of comparative validation with other questionnaires. On QSCC II have been written a paper of reliability, 2 papers of validation, and 2 papers of comparison of responses, a paper of standardization, a paper of comparison of another questionnaires, and a paper of simple questionnaires. On QSCC III have been written a paper of validation, and a paper of comparison of responses, a paper of comparison of another questionnaires. On QSCC II + have been written 3 papers of reliability and a paper of comparison of another questionnaires. After analying, the problems were two. One was the subject problem, the size (2 papers ; 9%) and homeostasis age/sex (13 papers ; 59%). Qnother was the constitution problem, 4 constitutions (20 paper ; 90.9%) and identifying indicator (17 papers ; 77.3%).

• Journal of Pharmaceutical Investigation
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• v.39 no.4
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• pp.263-267
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• 2009

The purpose of this study is to evaluate the efficacy of stipulated cleaning process, and the prohibition of cross-contamination and microbiological contamination, which inadequate cleaning in multi-production could occur, through cleaning validation of multi-purpose facility used to produce five biopharmaceutical products as sterile injection. After production of five biopharmaceutical products such as hGH, rhGCSF, rhEPO, rhFSH and rhIFN using vial filling machine, the cleaning validation such as residual analysis of active ingredients or human serum albumin, measurement of total organic carbon (TOC), residual analysis of detergent and microbiological contamination were carried out. In the case of rhGH and rhGCSF clean validations, drug residues were not detected. Furthermore, in the case of rhEPO, rhFSH and rhIFN clean validations, human serum albumin residues were not detected. At TOC (total organic carbon) analysis, all clean validations gave the TOC of about average 137.93%, not more than 150% of acceptance criteria. At sodium analysis for the checking of residues of cleaning agent, sodium residues were not detected. In sterility test, they showed no microbiological contamination of bacteria and fungi. Thus, this cleaning validation was determined as successful in protection of cross-contamination and induction of safety in multi-purpose facility.

• Journal of Applied Biological Chemistry
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• v.65 no.3
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• pp.195-202
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• 2022

Soybean (Glycine max L.) leaves have been researched as functional food stuff actively, but there is no validation method to control quality of soybean leaves (SL). In this study, we annotated seven kaempferol derivatives to confirm camelliaside A as index metabolite in SL using UHPLC-ESI-TOF-MS. HPLC-UVD validation method of camelliaside A in hot-water extract of SL was established according to validation guideline of functional foods from the Ministry of Food and Safety of Korea. The HPLC-UVD method was validated with reliable parameters for examining specificity, accuracy, precision, limit of detection and quantification and linearity. The established method gave the suitable ranges to qunatitate camelliaside A from the hot-water extract of soybean leaves.

• Journal of People, Plants, and Environment
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• v.22 no.6
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• pp.611-619
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• 2019

This study was conducted to investigate changes in the mental functions of the elderly in nursing facilities affected by a horticultural therapy program based on validation therapy. To meet the purpose, we conducted a horticultural therapy program based on validation therapy with 58 elderly participants (average age 79.12±6.84, men and women) once a week, 50 minutes per session, in a total of 10 sessions. Participants were divided into the control and experimental group with convenience sampling. The program was carried out from March 15 to November 22, 2018. Vibraimage 8 pro(ELSYS, 2014) is a recent, psychologically based, emotional-recognition visual imaging technology that measures pixels microvibration in terms of digital frequency and amplitude parameters. To examine the effects of the horticultural therapy program based on validation therapy, Vibraimage was used to assess aggression, stress, tension, suspect, balance, charm, energy, self-regulation, inhibition, neuroticism and positive, negative, physiological domains of mental functions before and after program. As a result, the mean score of the positive domain in the control group significantly decreased from 63.89±5.09 to 60.74±5.48, but it decreased without statistical significance in the experimental group from 63.98±5.45 to 61.39±6.02. The mean score of neuroticism in the experimental group significantly decreased from 31.64±10.94 to 22.87±13.79. Moreover, the mean score of the physiological domain in the experimental group also significantly decreased from 25.08±6.27 to 19.42±8.80. Accordingly, horticultural therapy program based on validation therapy can be utilized as a program to promote mental health, especially maintaining positive mental health function of the eldery, helping those who live in long-term care facilities enjoy a happier life more happier.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.442-453
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• 2021

Artificial intelligence (AI) will likely affect various fields of medicine. This article aims to explain the fundamental principles of clinical validation, device approval, and insurance coverage decisions of AI algorithms for medical diagnosis and prediction. Discrimination accuracy of AI algorithms is often evaluated with the Dice similarity coefficient, sensitivity, specificity, and traditional or free-response receiver operating characteristic curves. Calibration accuracy should also be assessed, especially for algorithms that provide probabilities to users. As current AI algorithms have limited generalizability to real-world practice, clinical validation of AI should put it to proper external testing and assisting roles. External testing could adopt diagnostic case-control or diagnostic cohort designs. A diagnostic case-control study evaluates the technical validity/accuracy of AI while the latter tests the clinical validity/accuracy of AI in samples representing target patients in real-world clinical scenarios. Ultimate clinical validation of AI requires evaluations of its impact on patient outcomes, referred to as clinical utility, and for which randomized clinical trials are ideal. Device approval of AI is typically granted with proof of technical validity/accuracy and thus does not intend to directly indicate if AI is beneficial for patient care or if it improves patient outcomes. Neither can it categorically address the issue of limited generalizability of AI. After achieving device approval, it is up to medical professionals to determine if the approved AI algorithms are beneficial for real-world patient care. Insurance coverage decisions generally require a demonstration of clinical utility that the use of AI has improved patient outcomes.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1141-1141
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• 2001

The aim of the study was to evaluate the performance of 3 calibration methods, modified partial least squares (MPLS), local PLS (LOCAL) and artificial neural network (ANN) on the prediction of chemical composition of forages, using a large NIR database. The study used forage samples (n=25,977) from Australia, Europe (Belgium, Germany, Italy and Sweden) and North America (Canada and U.S.A) with information relative to moisture, crude protein and neutral detergent fibre content. The spectra of the samples were collected with 10 different Foss NIR Systems instruments, which were either standardized or not standardized to one master instrument. The spectra were trimmed to a wavelength range between 1100 and 2498 nm. Two data sets, one standardized (IVAL) and the other not standardized (SVAL) were used as independent validation sets, but 10% of both sets were omitted and kept for later expansion of the calibration database. The remaining samples were combined into one database (n=21,696), which was split into 75% calibration (CALBASE) and 25% validation (VALBASE). The chemical components in the 3 validation data sets were predicted with each model derived from CALBASE using the calibration database before and after it was expanded with 10% of the samples from IVAL and SVAL data sets. Calibration performance was evaluated using standard error of prediction corrected for bias (SEP(C)), bias, slope and R2. None of the models appeared to be consistently better across all validation sets. VALBASE was predicted well by all models, with smaller SEP(C) and bias values than for IVAL and SVAL. This was not surprising as VALBASE was selected from the calibration database and it had a sample population similar to CALBASE, whereas IVAL and SVAL were completely independent validation sets. In most cases, Local and ANN models, but not modified PLS, showed considerable improvement in the prediction of IVAL and SVAL after the calibration database had been expanded with the 10% samples of IVAL and SVAL reserved for calibration expansion. The effects of sample processing, instrument standardization and differences in reference procedure were partially confounded in the validation sets, so it was not possible to determine which factors were most important. Further work on the development of large databases must address the problems of standardization of instruments, harmonization and standardization of laboratory procedures and even more importantly, the definition of the database population.

• Korean Journal of Microbiology
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• v.39 no.4
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• pp.242-247
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• 2003

Murine encephalomyocarditis virus (EMCV) has been used as a surrogate for hepatitis A virus (HAV) for the validation of virus removal and/or inactivation during the manufacturing process of biopharmaceuticals. Recently international regulation for the validation of HAV safety has been reinforced because of the reported cases of HAV transmission to hemophiliac patients who had received ntihemophilic factors prepared from human plasma. The purpose of the present study was to compare the resistance of HAV and EMCV to various viral inactivation processes and then to standardize the HAV validation method. HAV was more resistant than EMCV to pasteurization (60oC heat treatment for 10 hr), low pH incubation (pH 3.9 at 25oC for 14 days), 0.1 M NaOH treatment, and lyophilization. EMCV was completely inactivated to undetectable levels within 2 hr of pasteurization, however, HAV was completely inactivated to undetectable levels after 5 hr treatment. EMCV was completely inactivated to undetectable levels within 15 min of 0.1 M NaOH treatment, however, residual infectivity of HAV still remained even after 120 min of treatment. The log reduction factors achieved during low pH incubation were 1.63 for HAV and 3.84 for EMCV. Also the log reduction factors achieved during a lyophilization process of antihemophilic factor VIII were 1.21 for HAV and 4.57 for EMCV. These results indicate that HAV rather than EMCV should be used for the virus validation study and the validation results obtained using EMCV should be precisely reviewed.

• Journal of Korea Water Resources Association
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• v.41 no.10
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• pp.969-982
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• 2008

As hydrological models have been progressively developed, they are recognized as appropriate tools to manage water resources. Especially, the need to evaluate the effects of landuse and climate change on hydrological phenomena has been increased, which requires powerful validation methods for the hydrological models to be employed. As measured streamflow data at many locations may not be available, or include significant errors in application of hydrological models, streamflow data simulated by models only might be used to conduct hydrological analysis. In many cases, reducing errors in model simulations requires a powerful model validation method. In this research, we demonstrated a validation methodology of SWAT model using observed flow in two basins with different physical characteristics. First, we selected two basins, Gap-cheon basin and Yongdam basin located in the Guem River Basin, showing different hydrological characteristics. Next, the methodology developed to estimate parameter values for the Gap-cheon basin was applied for estimating those for the Yongdam basin without calibration a priori, and sought for validation of the SWAT. Application result with SWAT for Yongdam basin showed $R_{eff}$ ranging from 0.49 to 0.85, and $R^{2}$ from 0.49 to 0.84. As well, comparison of predicted flow and measured flow in each subbasin showed reasonable agreement. Furthermore, the model reproduced the whole trends of measured total flow and low flow, though peak flows were rather underestimated. The results of this study suggest that SWAT can be applied for predicting effects of future climate and landuse changes on flow variability in river basins. However, additional studies are recommended to further verify the validity of the mixed method in other river basins.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.577-587
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• 2023

The utilization of crowdsourced spatial data has been actively researched; however, issues stemming from the uncertainty of data quality have been raised. In particular, when low-quality data is mixed into drone imagery datasets, it can degrade the quality of spatial information output. In order to address these problems, the study presents a methodology for automatically validating the geometric quality of crowdsourced imagery. Key quality factors such as spatial resolution, resolution variation, matching point reprojection error, and bundle adjustment results are utilized. To classify imagery suitable for spatial information generation, training and validation datasets are constructed, and machine learning is conducted using a radial basis function (RBF)-based support vector machine (SVM) model. The trained SVM model achieved a classification accuracy of 99.1%. To evaluate the effectiveness of the quality validation model, imagery sets before and after applying the model to drone imagery not used in training and validation are compared by generating orthoimages. The results confirm that the application of the quality validation model reduces various distortions that can be included in orthoimages and enhances object identifiability. The proposed quality validation methodology is expected to increase the utility of crowdsourced data in spatial information generation by automatically selecting high-quality data from the multitude of crowdsourced data with varying qualities.

• Journal of Korean Academy of Nursing
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• v.35 no.6
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• pp.1063-1071
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• 2005

Purpose: This study was done to develop and test validity and reliability of on instrument for predicting nursing intention for SARS patient care. Method: The psychometric properties of a SARS patient care attrition prediction tool, based on the Theory of Planned Behavior, were examined in this study. The Three-phase design involved a) salient beliefs generated from clinical nurses (n=43) b) content validation by expert panel evaluations(n=5) c) face validation by plot testing (n=10) d) and instrument validation in a cross sectional survey (n=299). Psychometric analysis of survey data provided empirical evidence of the construct validity and reliability of the instrument. Result: Principal component analysis verified the hypothesized 6-factor solution, explaining $68.2\%$ of variance, and Alpha coefficients of .7538 to .9389 indicated a high internal consistency of the instrument. Conclusion: The instrument can be used by nurse administrators and researcher to assess clinical nurses' salient beliefs about caring for SARS patients, guide tailored intervention strategies to effective caring, and evaluate the effectiveness of interventions.