Pub Date : 1900-01-01DOI: 10.13188/2475-5591.1000016
HR Walker
Diabetes, well documented as a complicated condition, has been the focus of self-management studies for over three decades. Empowerment theory has co-developed within diabetes literature at the same time. However, this literature lacks a core and standard definition, which has led to incongruencies in theory and relative terminology. In this critical review, the construct of empowerment in diabetes literature is dissected and examined. Prominent measures and methods are problematized to highlight their overreliance on individual behavior rather than systemic social change. Current interventions targeting empowerment focus almost exclusively on individual behavior-change, inadvertently suggesting that the location of the problem of poor management lies within the abilities, attitudes, and beliefs of individuals. This paper argues that there has to be a socially-based power-related shift from one group to another in the process of empowerment for its construct to be complete, and that the ultimate agent of change must shift from the patient to systemic barriers in their way. Examples of online patient community-generated definitions, resources, and practices of empowerment are highlighted, leading to an argument that researchers and healthcare providers ought to add nuance to the construct of empowerment by weaving in community and systems levels change goals.
{"title":"Reimagining Empowerment: A Critical Review of Empowerment Theory in Diabetes Research","authors":"HR Walker","doi":"10.13188/2475-5591.1000016","DOIUrl":"https://doi.org/10.13188/2475-5591.1000016","url":null,"abstract":"Diabetes, well documented as a complicated condition, has been the focus of self-management studies for over three decades. Empowerment theory has co-developed within diabetes literature at the same time. However, this literature lacks a core and standard definition, which has led to incongruencies in theory and relative terminology. In this critical review, the construct of empowerment in diabetes literature is dissected and examined. Prominent measures and methods are problematized to highlight their overreliance on individual behavior rather than systemic social change. Current interventions targeting empowerment focus almost exclusively on individual behavior-change, inadvertently suggesting that the location of the problem of poor management lies within the abilities, attitudes, and beliefs of individuals. This paper argues that there has to be a socially-based power-related shift from one group to another in the process of empowerment for its construct to be complete, and that the ultimate agent of change must shift from the patient to systemic barriers in their way. Examples of online patient community-generated definitions, resources, and practices of empowerment are highlighted, leading to an argument that researchers and healthcare providers ought to add nuance to the construct of empowerment by weaving in community and systems levels change goals.","PeriodicalId":142531,"journal":{"name":"Advances in Diabetes & Endocrinology","volume":"354 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115781213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.13188/2475-5591.1000017
M. Fang
{"title":"A Case Report of Calcium- Sensing Receptor Gene Variant CASR (c.659G>A; p.R220Q) and Primary Hyperparathyroidism","authors":"M. Fang","doi":"10.13188/2475-5591.1000017","DOIUrl":"https://doi.org/10.13188/2475-5591.1000017","url":null,"abstract":"","PeriodicalId":142531,"journal":{"name":"Advances in Diabetes & Endocrinology","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114995791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.13188/2475-5591.1000018
JR Campione
Purpose: Clinical data provides the opportunity for efficient and timely disease surveillance. We developed and validated advanced phenotyping models to classify adult patients with diabetes to type 1, type 2, or other/indeterminate using structured fields from EHR data. To simulate the use of claims data supplemented with medication information, we compared model performance before and after the removal of body mass index (BMI) and laboratory results. Methods: We used 3 years of EHR data from a sample of 2,465 adult patients with diabetes from a health care system’s clinical data warehouse. A weighted ratio of type 1 diabetes codes to all diabetes codes was created by down-weighting codes from care settings that do not treat diabetes. We developed two multinomial regression models and a machine learning conditional inference tree to classify patients to type 1, type 2, or other/indeterminate. The models were validated by calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) relative to a gold standard. Results: For all models, the weighted ratio of type 1 diabetes was the strongest predictive factor. The models had validation statistics ≥ 93% for sensitivity; ≥ 87% for specificity; ≥ 88% for PPV, and ≥ 93% for NPV. After removal of BMI and laboratory data from the regression model the largest decline in performance from the full model was in type 2 diabetes specificity (90.8% to 89.2%). Conclusion: Prediction models and machine learning conditional inference trees using either structured fields from EHR data or claims data supplemented with medication data can be used to accurately distinguish diabetes type among adults. The inclusion of BMI and laboratory results improves model specificity for type 2
{"title":"Validated Models Using EHRs or Claims Data to Distinguish Diabetes Type among Adults","authors":"JR Campione","doi":"10.13188/2475-5591.1000018","DOIUrl":"https://doi.org/10.13188/2475-5591.1000018","url":null,"abstract":"Purpose: Clinical data provides the opportunity for efficient and timely disease surveillance. We developed and validated advanced phenotyping models to classify adult patients with diabetes to type 1, type 2, or other/indeterminate using structured fields from EHR data. To simulate the use of claims data supplemented with medication information, we compared model performance before and after the removal of body mass index (BMI) and laboratory results. Methods: We used 3 years of EHR data from a sample of 2,465 adult patients with diabetes from a health care system’s clinical data warehouse. A weighted ratio of type 1 diabetes codes to all diabetes codes was created by down-weighting codes from care settings that do not treat diabetes. We developed two multinomial regression models and a machine learning conditional inference tree to classify patients to type 1, type 2, or other/indeterminate. The models were validated by calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) relative to a gold standard. Results: For all models, the weighted ratio of type 1 diabetes was the strongest predictive factor. The models had validation statistics ≥ 93% for sensitivity; ≥ 87% for specificity; ≥ 88% for PPV, and ≥ 93% for NPV. After removal of BMI and laboratory data from the regression model the largest decline in performance from the full model was in type 2 diabetes specificity (90.8% to 89.2%). Conclusion: Prediction models and machine learning conditional inference trees using either structured fields from EHR data or claims data supplemented with medication data can be used to accurately distinguish diabetes type among adults. The inclusion of BMI and laboratory results improves model specificity for type 2","PeriodicalId":142531,"journal":{"name":"Advances in Diabetes & Endocrinology","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130792980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.13188/2475-5591.1000015
T. Endo
Glomerular hyperfiltration found in diabetic patients is recognized as an early renal alteration and reversible stage of kidney damage, but details have not yet been studied. We investigated the relation among estimated glomerular filtration rate (eGFR) and many factors such as age, HbA1cvalue, blood urea nitrogen (BUN) concentration, Na and K concentrations in serum of Japanese diabetic patients to investigate the characteristics of hyperfiltration [1]. Hyperfiltration (eGFR ≥ 120 ml/ mini/1.73 m 2 ) was found in six among 60 diabetic patients investigated, and Na concentrations in five patients with hyperfiltration were slightly
{"title":"Hyperfiltration in Diabetic Patients Associated With Mild Hyponatremia","authors":"T. Endo","doi":"10.13188/2475-5591.1000015","DOIUrl":"https://doi.org/10.13188/2475-5591.1000015","url":null,"abstract":"Glomerular hyperfiltration found in diabetic patients is recognized as an early renal alteration and reversible stage of kidney damage, but details have not yet been studied. We investigated the relation among estimated glomerular filtration rate (eGFR) and many factors such as age, HbA1cvalue, blood urea nitrogen (BUN) concentration, Na and K concentrations in serum of Japanese diabetic patients to investigate the characteristics of hyperfiltration [1]. Hyperfiltration (eGFR ≥ 120 ml/ mini/1.73 m 2 ) was found in six among 60 diabetic patients investigated, and Na concentrations in five patients with hyperfiltration were slightly","PeriodicalId":142531,"journal":{"name":"Advances in Diabetes & Endocrinology","volume":"1189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132064085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.13188/2475-5591.1000019
TG Singh
Diabetes mellitus is a disease which affects endocrine system and it is considered to beone of the most serious health problems to modern global health. Glycemic control is one of themost important aspects of preconception care; however other aspects such as folic acid supplementation, smoking cessation, screening and treatment of diabetes complications and discontinuing teratogenic medication, are as important for improving maternal and fetal outcomes. Effective preconception care is associated with improved pregnancy outcomes for women with diabetes. A multidisciplinary team work is essential for preconception care. Outcome becomes fruitful with patient awareness and managing diabetes before pregnancy in an organised manner.
{"title":"Diabetes Mellitus and Planning Conception","authors":"TG Singh","doi":"10.13188/2475-5591.1000019","DOIUrl":"https://doi.org/10.13188/2475-5591.1000019","url":null,"abstract":"Diabetes mellitus is a disease which affects endocrine system and it is considered to beone of the most serious health problems to modern global health. Glycemic control is one of themost important aspects of preconception care; however other aspects such as folic acid supplementation, smoking cessation, screening and treatment of diabetes complications and discontinuing teratogenic medication, are as important for improving maternal and fetal outcomes. Effective preconception care is associated with improved pregnancy outcomes for women with diabetes. A multidisciplinary team work is essential for preconception care. Outcome becomes fruitful with patient awareness and managing diabetes before pregnancy in an organised manner.","PeriodicalId":142531,"journal":{"name":"Advances in Diabetes & Endocrinology","volume":"67 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133085609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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