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4 results for “Scientific indicators”
Measuring output and impact of scientific journals in dentistry and oral medicine using bibliometric indices
<p>Dataset for all analyses done in the study</p>
Primary documentation on the scientific study of indicators of continuous monitoring and flash monitoring of glycemia in children and adolescents with type 1 diabetes mellitus
<p>The main purpose of creating an electronic database was to evaluate the performance of continuous glucose monitoring (CGM) and flash monitoring (FMS) in children and adolescents diagnosed with type 1 diabetes mellitus. The database is intended for entering, systematizing, storing and displaying patient data (date of birth, age, date of diagnosis of type 1 diabetes mellitus, length of illness, date of first visit to an endocrinologist, with the installation of a CGM or FMS), glycated hemoglobin indicators initially , during the study and ultimately, as well as CGM, FMS data (average glucose level, glycemic variability, percentage of cases above the target range, percentage of cases within the target range, percentage of cases below the target range, number of hypoglycemic episodes and their average duration, frequency of daily scans and frequency of sensor readings).</p><p>The database is the basis for comparative statistical analysis of dynamic monitoring indicators in groups of patients with the presence or absence of diabetic complications (neuropathy, retinopathy and nephropathy). The database presents the results of a prospective, open, controlled, clinical study obtained over a year and a half. The database includes information on 307 patients (adolescent children) aged 3 to 17 years inclusive. During the study, the observed patients were divided into two groups: Group 1 - patients diagnosed with type 1 diabetes mellitus and with diabetic complications, 152 people, Group 2 – patients diagnosed with type 1 diabetes mellitus and with no diabetic complications, 155 people. All registrants of the database were assigned individual codes, which made it possible to exclude personal data (full name) from the database.</p><p>The database is executed in the Microsoft Office Excel program and has the character of a depersonalized summary table, which consists of two blocks-sheets: patients of groups 1 and 2 and is structured according to the following sections: "Patient number"; "Patient code"; "Date of birth"; "Age of the patient"; section "Date of diagnosis of DM1" indicates the date of the official diagnosis of type 1 diabetes mellitus at the first hospitalization of the patient, this information is borrowed from medical information systems; section "Length of service DM1" reflects information about the duration of the patient's illness; the section "Date of the first visit" contains information about the date of the registrant's visit to the endocrinologist with the installation of FMS / CGM devices; the section "Frequency of self-monitoring with a glucometer" contains information about the frequency of measuring blood glucose levels by the patient at home using a glucometer until the establishment of FMS / CGM.</p><p>Sections "HbA1c initially (GMI)", "HbA1c (GMI)", "HbA1c final (GMI)", display the indicators of the level of glycated hemoglobin from the total for the period of the beginning of the study, at the intermediate stages of the study and at the end of observation.</p><p>The database structure has a number of sections accumulating information obtained with CGM/FMS, in particular: the section "Average glucose level"; the section "% above the target range", reflecting the percentage of the patient's stay with glycemia above the target indicators during the day; the section "% within the target range", reflecting the percentage of the patient's stay within the target glycemia indicators per day; the section "% below the target range", reflecting the percentage of the patient's stay with glycemia below the target indicators during the day; the section "Hypoglycemic phenomena", reflecting the number of cases of hypoglycemia in patients within 2 weeks; the section "Average duration", reflecting the average duration of hypoglycemic phenomena registered in the patient; the section "Sensor data received", indicating the percentage of time the patient was with an active device sensor; the section "Daily scans" show the frequency of scans of the patient's glycemic level (once a day); the section "%CV" displays the variability of the patient's glycemia recorded by the device. The listed sections are repeated in the database in accordance with the number of follow-up visit.</p><p>Also in the database there is a section "Mid. values", which contains indicators of the average values of patient data for all of the above sections, both in the first and in the second group of patients.</p><p>When working with the database, the use of filters (in the "Data" tab) containing the names of indicators allows you to enter information about new registrants in a convenient form or correct existing data, as well as sort and search for one or more specified indicators.</p><p>The electronic database allows you to systematize a large volume of results, distribute data into categories, search for any field or set of fields in the input format, systematize the selected array, makes it possible to directly use this data for statistical analysis, as well as to view and print information on specified conditions with the location of fields in a convenient sequence.</p>
Primary documentation on the scientific study of glycemic variability indicators in children and adolescents with diabetes mellitus type 1 diabetes
<p>The main purpose of creating an electronic database was to assess indicators of glycemic variability in adolescents and children diagnosed with diabetes mellitus type 1. The database presents the results of prospective, open, controlled, clinical study obtained over a period of one and a half years. Base data includes information on 307 patients (children and adolescents) aged 3 to 17 years inclusive. The observed patients were divided into two groups: Group 1 - patients diagnosed with type 1 diabetes mellitus and with diabetic complications, 152 people, Group 2 – patients diagnosed with type 1 diabetes mellitus and without diabetic complications, 155 people. All database registrants were assigned individual codes, which made it possible to exclude personal data (full name) from the database.</p><p>During the study, an analysis of carbohydrate metabolism was carried out with an assessment HbA1c (glycated hemoglobin), CGMS (continuous glucose monitoring), FMG (flash glucose monitoring) with face-to-face consultations with an endocrinologist and analysis of the data obtained. Obtained monitoring results were processed using a specialized variability calculator glycemia (EasyGV®, ver. 9), the following indicators and indices were calculated variability: average glycemic level (Mean), standard deviation (SD); index prolonged increase in glycemia (CONGA); glycemic lability index (LI); index risk of hypoglycemia (LBGI); hyperglycemia risk index (HBGI), average value overall risk (ADRR), average amplitude of glycemic fluctuations (MAGE); grade inter-day GV (MODD), rate of change in glycemia (MAG), J-index - indicator quality of glycemic control. The database is intended for entering, organizing, storing and displaying all of the above data.</p>
Integrative evaluation of biomonitoring data and modelings indicating atmospheric deposition of heavy metals, link to research data and scientific software
<p>Research data and scientific software related to integrative statistical analyses based on deposition data calculated with the model LOTOS-EUROS (LE) and the EMEP/MSC-East model (Germany, Europe) and Biomonitoring data on As, Cd, Cr, Cu, Ni, Pb, Zn concentrations in moss, leaves and needles and soil derived from the European Moss Survey (EMS), the German Environmental Specimen Bank (ESB) and the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests). The modelled HM deposition and respective concentrations in moss (EMS), leaves and needles (ESB, ICP Forests) and soil (ICP Forests) were investigated for their statistical relationships. Regression kriging was applied to calculate maps of Cd and Pb deposition across Germany.</p>
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