scholarly journals CONNECTIVE TISSUE HEART DYSPLASIA IN CHILDREN (LITERATURE REVIEW)

2021 ◽  
Vol 1 (2) ◽  
pp. 24-27
Author(s):  
Makhzuna Kholmuradovna Mukhsinova
Keyword(s):  
Early Detection ◽  
Connective Tissue ◽  
Literature Review ◽  
Patient Management ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Connective Tissue Dysplasia ◽  
Timely Treatment

This article highlights the main domestic and foreign publications on the problem of connective tissue dysplasia of the heart in children. An interpretation of minor heart anomalies is given. Currently, the collection of additional high-quality data on the course and treatment of children is important to identify the most effective algorithms for patient management. Proper monitoring, early detection of complications of the disease, and the possibility of timely treatment are essential.

scholarly journals Emergency Authorization of Chloroquine and Hydroxychloroquine for Treatment of COVID-19

2020 ◽  
Vol 54 (8) ◽  
pp. 827-831 ◽  
Author(s):  
Christopher R. Piszczatoski ◽  
Jason Powell
Keyword(s):  
Literature Review ◽  
Treatment Option ◽  
Safety Profile ◽  
Careful Consideration ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
The World ◽  
Experimental Treatments ◽  
Novel Coronavirus

The world is suffering a respiratory pandemic disease caused by a novel coronavirus (2019-nCoV), commonly known as COVID-19 (coronavirus disease 2019). The Food and Drug Administration issued an emergency authorization for chloroquine and hydroxychloroquine as experimental treatments for COVID-19 leading to a shortage of both medications. A literature review conducted in April 2020 shows a lack of high-quality data available, resulting in ambiguous guideline recommendations. Decisions to use either drug should be made with careful consideration of risks versus benefits along with proper monitoring. Because of its higher potency and better safety profile, hydroxychloroquine may be the more reasonable treatment option if treatment is initiated.

scholarly journals Personalized analytics and wearable biosensor platform for early detection of COVID-19 decompensation (DeCODe: Detection of COVID-19 Decompensation): protocol for development of COVID-19 Decompensation Index (Preprint)

2021 ◽  
Author(s):  
Karen Larimer
Keyword(s):  
Data Collection ◽  
Early Detection ◽  
Digital Health ◽  
World Health ◽  
Quality Data ◽  
Ordinal Scale ◽  
High Quality ◽  
High Quality Data ◽  
Digital Platform ◽  
Health Technologies

BACKGROUND During the COVID-19 pandemic, novel digital health technologies have the potential to improve our understanding of the SARS CO-V2 disease, improve care delivery and produce better health outcomes. The National Institutes of Health called on digital health leaders in this space to contribute to a high-quality data repository that will support the work of researchers to make discoveries not possible through small, limited data sets. OBJECTIVE To this end, we seek to develop a COVID-19 biomarker that could provide early detection of a patient’s physiologic decompensation. As a contributing spoke in this model, we propose developing and validating a COVID-19 Decompensation Index (CDI) in a two-phased project that builds off existing wearable biosensor-derived analytics generated by physIQ’s end-to-end cloud platform for continuous monitoring of physiology with wearable biosensors. This effort will achieve two primary objectives: 1) collect adequate data to enable the development of the CDI; and 2) collect rich deidentified clinical data correlative with outcomes and symptomology related to COVID-19 disease progression. Secondary objectives include evaluation of feasibility and usability of pinpointIQ™, the digital platform through which data is gathered, analyzed, and displayed. METHODS This study is a prospective, non-randomized, open-label, two-phase design. Phase I will involve data collection for the NIH digital data hub as well as data to support the preliminary development of the CDI. Phase II will involve data collection for the hub and contribute to continued refinement and validation of the CDI. While this study will focus on development of a CDI, the digital platform will also be evaluated for feasibility and usability while clinicians deliver care to continuously monitored patients enrolled in the study. RESULTS Our target COVID-19 Decompensation Index (CDI) will be a binary classifier trained to distinguish between subjects decompensating and not decompensating. The primary performance metric for CDI will be ROC AUC with a minimum performance criterion of AUC ≥ 0.75 (significance α = 0.05 and power 1 – β = 0.80). Determination of sex/gender, race or ethnic characteristics that impact differences in the CDI performance, as well as lead time-time to predict decompensation and the relationship to ultimate severity of disease based on the World Health Organization COVID-19 Ordinal Scale will be explored. CONCLUSIONS Using machine learning techniques on a large data set of COVID-19 positive patients could produce valuable insights into the physiology of COVID-19 as well as a digital biomarker for COVID-19 decompensation. We plan, with this study, to develop a tool that can uniquely reflect the physiologic data of a diverse population and contribute to a trove of high-quality data that will help researchers better understand COVID-19. CLINICALTRIAL Trial Registration: ClinicalTrials.gov NCT NCT04575532

Collecting High-Quality Data

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Social Desirability ◽  
Measurement Errors ◽  
Monitoring And Evaluation ◽  
Quality Data ◽  
Social Desirability Bias ◽  
High Quality ◽  
High Quality Data ◽  
Evaluation Systems ◽  
Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

Developmental Impacts of Remittances on Migrant-Sending Households: Micro-Level Evidence from Punjab, Pakistan

2019 ◽  
Vol 14 (3) ◽  
pp. 338-366
Author(s):  
Kashif Imran ◽  
Evelyn S. Devadason ◽  
Cheong Kee Cheok
Keyword(s):  
Human Development ◽  
Human Development Index ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Development Index ◽  
Policy Interventions ◽  
Education And Health ◽  
Micro Level ◽  
The Government

This article analyzes the overall and type of developmental impacts of remittances for migrant-sending households (HHs) in districts of Punjab, Pakistan. For this purpose, an HH-based human development index is constructed based on the dimensions of education, health and housing, with a view to enrich insights into interactions between remittances and HH development. Using high-quality data from a HH micro-survey for Punjab, the study finds that most migrant-sending HHs are better off than the HHs without this stream of income. More importantly, migrant HHs have significantly higher development in terms of housing in most districts of Punjab relative to non-migrant HHs. Thus, the government would need policy interventions focusing on housing to address inequalities in human development at the district-HH level, and subsequently balance its current focus on the provision of education and health.

Capturing sexual assault data: An information system designed by forensic clinicians and healthcare researchers

2017 ◽  
Vol 47 (1) ◽  
pp. 46-55 ◽  
Author(s):  
S Aqif Mukhtar ◽  
Debbie A Smith ◽  
Maureen A Phillips ◽  
Maire C Kelly ◽  
Renate R Zilkens ◽  
...  
Keyword(s):  
Information System ◽  
Sexual Assault ◽  
Data Management ◽  
Sexual Violence ◽  
Western Australia ◽  
Management System ◽  
Data Management System ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data

Background: The Sexual Assault Resource Center (SARC) in Perth, Western Australia provides free 24-hour medical, forensic, and counseling services to persons aged over 13 years following sexual assault. Objective: The aim of this research was to design a data management system that maintains accurate quality information on all sexual assault cases referred to SARC, facilitating audit and peer-reviewed research. Methods: The work to develop SARC Medical Services Clinical Information System (SARC-MSCIS) took place during 2007–2009 as a collaboration between SARC and Curtin University, Perth, Western Australia. Patient demographics, assault details, including injury documentation, and counseling sessions were identified as core data sections. A user authentication system was set up for data security. Data quality checks were incorporated to ensure high-quality data. Results: An SARC-MSCIS was developed containing three core data sections having 427 data elements to capture patient’s data. Development of the SARC-MSCIS has resulted in comprehensive capacity to support sexual assault research. Four additional projects are underway to explore both the public health and criminal justice considerations in responding to sexual violence. The data showed that 1,933 sexual assault episodes had occurred among 1881 patients between January 1, 2009 and December 31, 2015. Sexual assault patients knew the assailant as a friend, carer, acquaintance, relative, partner, or ex-partner in 70% of cases, with 16% assailants being a stranger to the patient. Conclusion: This project has resulted in the development of a high-quality data management system to maintain information for medical and forensic services offered by SARC. This system has also proven to be a reliable resource enabling research in the area of sexual violence.

We Need High Quality Data in Our Diversity Articles

2021 ◽  
Author(s):  
Daniel B. Chonde ◽  
Seifu J. Chonde
Keyword(s):  
Quality Data ◽  
High Quality ◽  
High Quality Data

scholarly journals Fooled by Performance Randomness: Overrewarding Luck

2019 ◽  
Vol 101 (4) ◽  
pp. 658-666 ◽  
Author(s):  
Romain Gauriot ◽  
Lionel Page
Keyword(s):  
Causal Effect ◽  
Experimental Situation ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Economic Organizations ◽  
Quasi Experimental ◽  
Similar Location ◽  
Do So

We provide evidence of a violation of the informativeness principle whereby lucky successes are overly rewarded. We isolate a quasi-experimental situation where the success of an agent is as good as random. To do so, we use high-quality data on football (soccer) matches and select shots on goal that landed on the goal posts. Using nonscoring shots, taken from a similar location on the pitch, as counterfactuals to scoring shots, we estimate the causal effect of a lucky success (goal) on the evaluation of the player's performance. We find clear evidence that luck is overly influencing managers' decisions and evaluators' ratings. Our results suggest that this phenomenon is likely to be widespread in economic organizations.

scholarly journals R factors in Rietveld analysis: How good is good enough?

2006 ◽  
Vol 21 (1) ◽  
pp. 67-70 ◽  
Author(s):  
Brian H. Toby
Keyword(s):  
Rietveld Analysis ◽  
Poor Quality ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Poor Quality Data ◽  
Error Index ◽  
R Factors ◽  
Very High

The definitions for important Rietveld error indices are defined and discussed. It is shown that while smaller error index values indicate a better fit of a model to the data, wrong models with poor quality data may exhibit smaller values error index values than some superb models with very high quality data.

scholarly journals AGRIBALYSE®, the French LCI Database for agricultural products: high quality data for producers and environmental labelling

OCL ◽  
2014 ◽  
Vol 22 (1) ◽  
pp. D104 ◽  
Author(s):  
Vincent Colomb ◽  
Samy Ait Amar ◽  
Claudine Basset Mens ◽  
Armelle Gac ◽  
Gérard Gaillard ◽  
...  
Keyword(s):  
Agricultural Products ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Environmental Labelling

scholarly journals A Study on Pattern Recognition with the Histograms of Oriented Gradients in Distorted and Noisy Images

2020 ◽  
Vol 26 (4) ◽  
pp. 454-478
Author(s):  
Andrzej Bukała ◽  
Michał Koziarski ◽  
Bogusław Cyganek ◽  
Osman Koç ◽  
Alperen Kara
Keyword(s):  
Pattern Recognition ◽  
Negative Impact ◽  
Quality Data ◽  
High Quality ◽  
Denoising Method ◽  
High Quality Data ◽  
Histograms Of Oriented Gradients ◽  
Different Types ◽  
Simple Implementation ◽  
Salt And Pepper

Histograms of oriented gradients (HOG) are still one of the most frequently used low-level features for pattern recognition in images. Despite their great popularity and simple implementation performance of the HOG features almost always has been measured on relatively high quality data which are far from real conditions. To fill this gap we experimentally evaluate their performance in the more realistic conditions, based on images affected by different types of noise, such as Gaussian, quantization, and salt-and-pepper, as well on images distorted by occlusions. Different noise scenarios were tested such anti-distortions during training as well as application of a proper denoising method in the recognition stage. As underpinned with experimental results, the negative impact of distortions and noise on object recognition with HOG features can be significantly reduced by employment of a proper denoising strategy.

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