scholarly journals A Validation Study of NOVA Classification for Ultra-Processed Food on the USDA Food and Nutrient Database

2021 ◽  
Vol 5 (Supplement_2) ◽  
pp. 594-594
Author(s):  
Giulia Lorenzoni ◽  
Rita Di Benedetto ◽  
Honoria Ocagli ◽  
Dario Gregori ◽  
Marco Silano
Keyword(s):  
Validation Study ◽  
Food Processing ◽  
Cultural Context ◽  
Food Composition ◽  
Processed Food ◽  
Cohen’S Kappa ◽  
Cultural Validity ◽  
Funding Sources ◽  
Food Components ◽  
Cohen's Kappa

Abstract Objectives In the last years, a group of Brazilian researchers has proposed a new food classification system, named NOVA, based on the extent of food processing. However, the feasibility of such classification has been debated, together with its cross-cultural validity. The present work assessed the NOVA classification feasibility, conducting a validation study on the USDA Food Composition Database. Methods Two independent reviewers rated each food reported in the 2015–2016 USDA Food and Nutrient Database to be or not ultra-processed food (UPF) according to the criteria presented in the manuscripts published by Monteiro CA et al. in 2016 and 2019 presenting the NOVA classification. A third independent reviewer solved disagreements. The Cohen's Kappa was calculated to evaluate the agreement between the two independent reviewers. Results The agreement between the reviewers was only moderate, with a Cohen's Kappa of 0.58. The disagreement pertained mainly the mixed dishes since it was difficult for the two independent reviewers to rate (UPF or not) the single food components of the mixed dishes. Conclusions Such work provides insights on the difficulties encountered in applying the NOVA classification to a real-word food database outside the cultural context in which the NOVA classification was developed. Funding Sources N/A.

scholarly journals An update on image forming methods: structure analysis and Gestalt evaluation of images from rocket lettuce with shading, N supply, organic or mineral fertilization, and biodynamic preparations

2021 ◽  
Author(s):  
Miriam Athmann ◽  
Roya Bornhütter ◽  
Nicolaas Busscher ◽  
Paul Doesburg ◽  
Uwe Geier ◽  
...  
Keyword(s):  
Copper Chloride ◽  
Organic Fertilizer ◽  
Structural Features ◽  
Fertilizer Treatment ◽  
Friedman Test ◽  
Mineral Fertilization ◽  
Cohen’S Kappa ◽  
N Supply ◽  
Cohen's Kappa ◽  
Forming Methods

AbstractIn the image forming methods, copper chloride crystallization (CCCryst), capillary dynamolysis (CapDyn), and circular chromatography (CChrom), characteristic patterns emerge in response to different food extracts. These patterns reflect the resistance to decomposition as an aspect of resilience and are therefore used in product quality assessment complementary to chemical analyses. In the presented study, rocket lettuce from a field trial with different radiation intensities, nitrogen supply, biodynamic, organic and mineral fertilization, and with or without horn silica application was investigated with all three image forming methods. The main objective was to compare two different evaluation approaches, differing in the type of image forming method leading the evaluation, the amount of factors analyzed, and the deployed perceptual strategy: Firstly, image evaluation of samples from all four experimental factors simultaneously by two individual evaluators was based mainly on analyzing structural features in CapDyn (analytical perception). Secondly, a panel of eight evaluators applied a Gestalt evaluation imbued with a kinesthetic engagement of CCCryst patterns from either fertilization treatments or horn silica treatments, followed by a confirmatory analysis of individual structural features. With the analytical approach, samples from different radiation intensities and N supply levels were identified correctly in two out of two sample sets with groups of five samples per treatment each (Cohen’s kappa, p = 0.0079), and the two organic fertilizer treatments were differentiated from the mineral fertilizer treatment in eight out of eight sample sets with groups of three manure and two minerally fertilized samples each (Cohen’s kappa, p = 0.0048). With the panel approach based on Gestalt evaluation, biodynamic fertilization was differentiated from organic and mineral fertilization in two out of two exams with 16 comparisons each (Friedman test, p < 0.001), and samples with horn silica application were successfully identified in two out of two exams with 32 comparisons each (Friedman test, p < 0.001). Further research will show which properties of the food decisive for resistance to decomposition are reflected by analytical and Gestalt criteria, respectively, in CCCryst and CapDyn images.

scholarly journals Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexandre Maciel-Guerra ◽  
Necati Esener ◽  
Katharina Giebel ◽  
Daniel Lea ◽  
Martin J. Green ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Treatment Success ◽  
Clinical Mastitis ◽  
Supervised Machine Learning ◽  
Structural Protein ◽  
Cohen’S Kappa ◽  
Maldi Tof ◽  
Streptococcus Uberis ◽  
Cohen's Kappa

AbstractStreptococcus uberis is one of the leading pathogens causing mastitis worldwide. Identification of S. uberis strains that fail to respond to treatment with antibiotics is essential for better decision making and treatment selection. We demonstrate that the combination of supervised machine learning and matrix-assisted laser desorption ionization/time of flight (MALDI-TOF) mass spectrometry can discriminate strains of S. uberis causing clinical mastitis that are likely to be responsive or unresponsive to treatment. Diagnostics prediction systems trained on 90 individuals from 26 different farms achieved up to 86.2% and 71.5% in terms of accuracy and Cohen’s kappa. The performance was further increased by adding metadata (parity, somatic cell count of previous lactation and count of positive mastitis cases) to encoded MALDI-TOF spectra, which increased accuracy and Cohen’s kappa to 92.2% and 84.1% respectively. A computational framework integrating protein–protein networks and structural protein information to the machine learning results unveiled the molecular determinants underlying the responsive and unresponsive phenotypes.

scholarly journals The accuracy of initial diagnoses in coma: an observational study in 835 patients with non-traumatic disorder of consciousness

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Maximilian Lutz ◽  
Martin Möckel ◽  
Tobias Lindner ◽  
Christoph J. Ploner ◽  
Mischa Braun ◽  
...  
Keyword(s):  
Emergency Care ◽  
Final Diagnosis ◽  
Class I ◽  
Prehospital Emergency Care ◽  
Unknown Etiology ◽  
Care Providers ◽  
Cohen’S Kappa ◽  
Disorder Of Consciousness ◽  
Cohen's Kappa ◽  
Suspected Diagnosis

Abstract Background Management of patients with coma of unknown etiology (CUE) is a major challenge in most emergency departments (EDs). CUE is associated with a high mortality and a wide variety of pathologies that require differential therapies. A suspected diagnosis issued by pre-hospital emergency care providers often drives the first approach to these patients. We aim to determine the accuracy and value of the initial diagnostic hypothesis in patients with CUE. Methods Consecutive ED patients presenting with CUE were prospectively enrolled. We obtained the suspected diagnoses or working hypotheses from standardized reports given by prehospital emergency care providers, both paramedics and emergency physicians. Suspected and final diagnoses were classified into I) acute primary brain lesions, II) primary brain pathologies without acute lesions and III) pathologies that affected the brain secondarily. We compared suspected and final diagnosis with percent agreement and Cohen’s Kappa including sub-group analyses for paramedics and physicians. Furthermore, we tested the value of suspected and final diagnoses as predictors for mortality with binary logistic regression models. Results Overall, suspected and final diagnoses matched in 62% of 835 enrolled patients. Cohen’s Kappa showed a value of κ = .415 (95% CI .361–.469, p < .005). There was no relevant difference in diagnostic accuracy between paramedics and physicians. Suspected diagnoses did not significantly interact with in-hospital mortality (e.g., suspected class I: OR .982, 95% CI .518–1.836) while final diagnoses interacted strongly (e.g., final class I: OR 5.425, 95% CI 3.409–8.633). Conclusion In cases of CUE, the suspected diagnosis is unreliable, regardless of different pre-hospital care providers’ qualifications. It is not an appropriate decision-making tool as it neither sufficiently predicts the final diagnosis nor detects the especially critical comatose patient. To avoid the risk of mistriage and unnecessarily delayed therapy, we advocate for a standardized diagnostic work-up for all CUE patients that should be triggered by the emergency symptom alone and not by any suspected diagnosis.

scholarly journals Classification of Shoulder X-ray Images with Deep Learning Ensemble Models

2021 ◽  
Vol 11 (6) ◽  
pp. 2723
Author(s):  
Fatih Uysal ◽  
Fırat Hardalaç ◽  
Ozan Peker ◽  
Tolga Tolunay ◽  
Nil Tokgöz
Keyword(s):  
Deep Learning ◽  
Performance Test ◽  
The Body ◽  
Test Accuracy ◽  
Cohen’S Kappa ◽  
X Ray ◽  
Cohen's Kappa ◽  
Auc Value ◽  
Magnetic Resonance Imaging Mri ◽  
Fully Connected

Fractures occur in the shoulder area, which has a wider range of motion than other joints in the body, for various reasons. To diagnose these fractures, data gathered from X-radiation (X-ray), magnetic resonance imaging (MRI), or computed tomography (CT) are used. This study aims to help physicians by classifying shoulder images taken from X-ray devices as fracture/non-fracture with artificial intelligence. For this purpose, the performances of 26 deep learning-based pre-trained models in the detection of shoulder fractures were evaluated on the musculoskeletal radiographs (MURA) dataset, and two ensemble learning models (EL1 and EL2) were developed. The pre-trained models used are ResNet, ResNeXt, DenseNet, VGG, Inception, MobileNet, and their spinal fully connected (Spinal FC) versions. In the EL1 and EL2 models developed using pre-trained models with the best performance, test accuracy was 0.8455, 0.8472, Cohen’s kappa was 0.6907, 0.6942 and the area that was related with fracture class under the receiver operating characteristic (ROC) curve (AUC) was 0.8862, 0.8695. As a result of 28 different classifications in total, the highest test accuracy and Cohen’s kappa values were obtained in the EL2 model, and the highest AUC value was obtained in the EL1 model.

scholarly journals The South African dysphagia screening tool (SADS): A screening tool for a developing context

2016 ◽  
Vol 63 (1) ◽  
Author(s):  
Calli Ostrofsky ◽  
Jaishika Seedat
Keyword(s):  
At Risk ◽  
Acute Stroke ◽  
South African ◽  
Screening Tool ◽  
Diagnostic Assessment ◽  
The South ◽  
Stroke Patients ◽  
Cohen’S Kappa ◽  
Cohen's Kappa ◽  
Government Hospitals

Background: Notwithstanding its value, there are challenges and limitations to implementing a dysphagia screening tool from a developed contexts in a developing context. The need for a reliable and valid screening tool for dysphagia that considers context, systemic rules and resources was identified to prevent further medical compromise, optimise dysphagia prognosis and ultimately hasten patients’ return to home or work.Methodology: To establish the validity and reliability of the South African dysphagia screening tool (SADS) for acute stroke patients accessing government hospital services. The study was a quantitative, non-experimental, correlational cross-sectional design with a retrospective component. Convenient sampling was used to recruit 18 speech-language therapists and 63 acute stroke patients from three South African government hospitals. The SADS consists of 20 test items and was administered by speech-language therapists. Screening was followed by a diagnostic dysphagia assessment. The administrator of the tool was not involved in completing the diagnostic assessment, to eliminate bias and prevent contamination of results from screener to diagnostic assessment. Sensitivity, validity and efficacy of the screening tool were evaluated against the results of the diagnostic dysphagia assessment. Cohen’s kappa measures determined inter-rater agreement between the results of the SADS and the diagnostic assessment.Results and conclusion: The SADS was proven to be valid and reliable. Cohen’s kappa indicated a high inter-rater reliability and showed high sensitivity and adequate specificity in detecting dysphagia amongst acute stroke patients who were at risk for dysphagia. The SADS was characterised by concurrent, content and face validity. As a first step in establishing contextual appropriateness, the SADS is a valid and reliable screening tool that is sensitive in identifying stroke patients at risk for dysphagia within government hospitals in South Africa.

Deep Learning–Based Automated Thrombolysis in Cerebral Infarction Scoring: A Timely Proof-of-Principle Study

Stroke ◽  
2021 ◽  
Author(s):  
Maximilian Nielsen ◽  
Moritz Waldmann ◽  
Andreas M. Frölich ◽  
Fabian Flottmann ◽  
Evelin Hristova ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cerebral Infarction ◽  
Digital Subtraction Angiography ◽  
Information Integration ◽  
Mechanical Thrombectomy ◽  
Model Updating ◽  
Digital Subtraction ◽  
Cohen’S Kappa ◽  
Expert Rating ◽  
Cohen's Kappa

Background and Purpose: Mechanical thrombectomy is an established procedure for treatment of acute ischemic stroke. Mechanical thrombectomy success is commonly assessed by the Thrombolysis in Cerebral Infarction (TICI) score, assigned by visual inspection of X-ray digital subtraction angiography data. However, expert-based TICI scoring is highly observer-dependent. This represents a major obstacle for mechanical thrombectomy outcome comparison in, for instance, multicentric clinical studies. Focusing on occlusions of the M1 segment of the middle cerebral artery, the present study aimed to develop a deep learning (DL) solution to automated and, therefore, objective TICI scoring, to evaluate the agreement of DL- and expert-based scoring, and to compare corresponding numbers to published scoring variability of clinical experts. Methods: The study comprises 2 independent datasets. For DL system training and initial evaluation, an in-house dataset of 491 digital subtraction angiography series and modified TICI scores of 236 patients with M1 occlusions was collected. To test the model generalization capability, an independent external dataset with 95 digital subtraction angiography series was analyzed. Characteristics of the DL system were modeling TICI scoring as ordinal regression, explicit consideration of the temporal image information, integration of physiological knowledge, and modeling of inherent TICI scoring uncertainties. Results: For the in-house dataset, the DL system yields Cohen’s kappa, overall accuracy, and specific agreement values of 0.61, 71%, and 63% to 84%, respectively, compared with the gold standard: the expert rating. Values slightly drop to 0.52/64%/43% to 87% when the model is, without changes, applied to the external dataset. After model updating, they increase to 0.65/74%/60% to 90%. Literature Cohen’s kappa values for expert-based TICI scoring agreement are in the order of 0.6. Conclusions: The agreement of DL- and expert-based modified TICI scores in the range of published interobserver variability of clinical experts highlights the potential of the proposed DL solution to automated TICI scoring.

Automated Large Artery Occlusion Detection in Stroke: A Single-Center Validation Study of an Artificial Intelligence Algorithm

2021 ◽  
pp. 1-6
Author(s):  
Gabriel Rodrigues ◽  
Clara M. Barreira ◽  
Mehdi Bouslama ◽  
Diogo C. Haussen ◽  
Alhamza Al-Bayati ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Secondary Analysis ◽  
Large Artery ◽  
Single Center ◽  
Characteristic Analysis ◽  
Cohen’S Kappa ◽  
Accurate Identification ◽  
Vessel Occlusion ◽  
Test Characteristic ◽  
Cohen's Kappa

<b><i>Introduction:</i></b> Expediting notification of lesions in acute ischemic stroke (AIS) is critical. Limited availability of experts to assess such lesions and delays in large vessel occlusion (LVO) recognition can negatively affect outcomes. Artificial intelligence (AI) may aid LVO recognition and treatment. This study aims to evaluate the performance of an AI-based algorithm for LVO detection in AIS. <b><i>Methods:</i></b> Retrospective analysis of a database of AIS patients admitted in a single center between 2014 and 2019. Vascular neurologists graded computed tomography angiographies (CTAs) for presence and site of LVO. Studies were analyzed by the Viz-LVO Algorithm® version 1.4 – neural network programmed to detect occlusions from the internal carotid artery terminus (ICA-T) to the Sylvian fissure. Comparisons between human versus AI-based readings were done by test characteristic analysis and Cohen’s kappa. Primary analysis included ICA-T and/or middle cerebral artery (MCA)-M1 LVOs versus non-LVOs/more distal occlusions. Secondary analysis included MCA-M2 occlusions. <b><i>Results:</i></b> 610 CTAs were analyzed. The AI algorithm rejected 2.5% of the CTAs due to poor quality, which were excluded from the analysis. Viz-LVO identified ICA-T and MCA-M1 LVOs with a sensitivity of 87.6%, specificity of 88.5%, and accuracy of 87.9% (AUC 0.88, 95% CI: 0.85–0.92, <i>p</i> &#x3c; 0.001). Cohen’s kappa was 0.74. In the secondary analysis, the algorithm yielded a sensitivity of 80.3%, specificity of 88.5%, and accuracy of 82.7%. The mean run time of the algorithm was 2.78 ± 0.5 min. <b><i>Conclusion:</i></b> Automated AI reading allows for fast and accurate identification of LVO strokes with timely notification to emergency teams, enabling quick decision-making for reperfusion therapies or transfer to specialized centers if needed.

scholarly journals Ruídos articulares e sinais de disfunção temporomandibular: um estudo comparativo por meio de palpação manual e vibratografia computadorizada da ATM

2000 ◽  
Vol 14 (4) ◽  
pp. 367-371 ◽  
Author(s):  
Paulo César Rodrigues CONTI ◽  
João Evandro da Silva MIRANDA ◽  
Flávia ORNELAS
Keyword(s):  
Cohen’S Kappa ◽  
Cohen's Kappa

Esta pesquisa teve como finalidade estimar a validade interexaminadores, em detectar sons articulares e comparar os resultados com um sistema computadorizado (SONOPAK). Para isto, uma amostra de 45 indivíduos foi selecionada aleatoriamente e dividida em dois grupos. O grupo experimental foi formado por 24 pacientes, que apresentavam problemas articulares, e o grupo controle por 19 pacientes com ausência de qualquer relato ou queixa, compatível com DTM. Sessenta e sete por cento dos pacientes eram mulheres, com médias de idade de 36 anos. Os resultados da eletrovibratografia (EVG) foram comparados com os obtidos pela palpação manual. Todos os examinadores desconheciam o grupo ao qual pertencia o paciente examinado. Para análise dos resultados de concordância, foi utilizado o teste de Cohen’s kappa e o percentual de concordância. Os resultados mostraram uma prevalência de 62,5% e 42,1% dos sons articulares, apresentados pelo grupo experimental e grupo controle, respectivamente. Pela análise dos resultados, concluiu-se que os sons articulares são comumente apresentados em ambos os grupos, porém sua identificação e classificação são difíceis, mesmo quando obtidos por aparelhos computadorizados. Embora a amostra deste estudo seja pequena, os resultados indicam que a EVG deve ser utilizada com cautela, e a calibração dos examinadores pode melhorar a identificação dos sons articulares.

scholarly journals Fundamental Tactical Principles of Soccer: a Comparison of Different Age Groups

2017 ◽  
Vol 58 (1) ◽  
pp. 207-214 ◽  
Author(s):  
Paulo Henrique Borges ◽  
José Guilherme ◽  
Leandro Rechenchosky ◽  
Luciane Cristina Arantes da Costa ◽  
Wilson Rinadi
Keyword(s):  
Age Groups ◽  
Progressive Increase ◽  
Soccer Players ◽  
Study Analysis ◽  
Cohen’S Kappa ◽  
Youth Soccer ◽  
Cohen's Kappa

AbstractThe fundamental tactical principles of the game of soccer represent a set of action rules that guide behaviours related to the management of game space. The aim of this study was to compare the performance of fundamental offensive and defensive tactical principles among youth soccer players from 12 to 17 years old. The sample consisted of 3689 tactical actions performed by 48 soccer players in three age categories: under 13 (U-13), under 15 (U-15), and under 17 (U-17). Tactical performance was measured using the System of Tactical Assessment in Soccer (FUT-SAT). The Kruskal Wallis, Mann-Whitney U, Friedman, Wilcoxon, and Cohen’s Kappa tests were used in the study analysis. The results showed that the principles of “offensive coverage” (p = 0.01) and “concentration” (p = 0.04) were performed more frequently by the U-17 players than the U-13 players. The tactical principles “width and length” (p < 0.05) and “defensive unit” (p < 0.05) were executed more frequently by younger soccer players. It can be concluded that the frequency with which fundamental tactical principles are performed varies between the gaming categories, which implies that there is valuation of defensive security and a progressive increase in “offensive coverage” caused by increased confidence and security in offensive actions.

scholarly journals The reality of assessing ‘authentic’ electronic portfolios: Can electronic portfolios serve as a form of standardized assessment to measure literacy and self-regulated learning at the elementary level? / L’évaluation d’e-portfolio «authentiques»

2013 ◽  
Vol 39 (4) ◽  
Author(s):  
Eva Mary Bures ◽  
Alexandra Barclay ◽  
Philip C Abrami ◽  
Elizabeth J Meyer
Keyword(s):  
Learning Strategies ◽  
Electronic Portfolios ◽  
Assessment Tools ◽  
First Year ◽  
Cohen’S Kappa ◽  
Self Regulated Learning ◽  
Cohen's Kappa ◽  
Regulated Learning ◽  
The Government ◽  
Literacy Scores

This study explores electronic portfolios and their potential to assess student literacy and self-regulated learning in elementary-aged children. Assessment tools were developed and include a holistic rubric that assigns a mark from 1 to 5 to self-regulated learning (SRL) and a mark to literacy, and an analytical rubric measuring multiple sub-scales of SRL and literacy. Participants in grades 4, 5 and 6 across two years created electronic portfolios, with n=369 volunteers. Some classes were excluded from statistical analyses in the first year due to low implementation and some individuals were excluded in both years, leaving n=251 included in analyses. All portfolios were coded by two coders, and the inter-rater reliability explored. During the first year Cohen’s kappa ranged from 0.70 to 0.79 for literacy and SRL overall, but some sub-scales were unacceptably weak. The second year showed improvement in Cohen’s kappa overall and especially for the sub-scales, reflecting improved implementation of the portfolios and use of the assessment tools. Validity was explored by comparing the relationship of portfolio scores to other measures, including the government scores on the open-response literacy questions for the Canadian Achievement Tests (version 4), the scores we assigned to the CAT-4s using our assessment tools, and scores on the Student Learning Strategies Questionnaire (SLSQ) measuring SRL. The portfolio literacy scores correlated (p

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