A PROSPECTIVE STUDY OF IATROGENIC FACIAL NERVE PALSY: THE LEARNING INSIGHTS.

Introduction This study aims to review the causative factors and various treatment modalities for iatrogenic facial nerve palsy.  Methods This is an analytical prospective study carried out at a tertiary care hospital in Department of ENT at Seth GS Medical College & KEM Hospital between Jan 2013 to Jan 2017 with a sample size of 12 patients.  Results 6 patients developed iatrogenic facial nerve palsy after mastoidectomy, 3 secondaries to parotidectomy, 1 post stapedotomy, 1 after cochlear implant and 1 secondary to acoustic neuroma excision. 7 out of them were surgically re-explored and rest were given conservative management. 10 patients had partial recovery and 2 of them have complete return of facial nerve function.  Conclusion There is no substitute for thorough knowledge of anatomy of facial nerve during otologic surgeries. In cases of iatrogenic facial nerve palsy surgical exploration should be considered when there is an immediate onset complete facial nerve palsy.  Recommendation Intraoperative facial nerve monitoring is a recommended technique during mastoidectomy, despite the fact that it cannot replace anatomical identification of the facial nerve or surgical expertis

Automated Knowledge Modeling for Cancer Clinical Practice Guidelines

Clinical Practice Guidelines (CPGs) for cancer diseases evolve rapidly due to new evidence generated by active research. Currently, CPGs are primarily published in a document format that is ill-suited for managing this developing knowledge. A knowledge model of the guidelines document suitable for programmatic interaction is required. This work proposes an automated method for extraction of knowledge from National Comprehensive Cancer Network (NCCN) CPGs in Oncology and generating a structured model containing the retrieved knowledge. The proposed method was tested using two versions of NCCN Non-Small Cell Lung Cancer (NSCLC) CPG to demonstrate the effectiveness in faithful extraction and modeling of knowledge. Three enrichment strategies using Cancer staging information, Unified Medical Language System (UMLS) Metathesaurus & National Cancer Institute thesaurus (NCIt) concepts, and Node classification are also presented to enhance the model towards enabling programmatic traversal and querying of cancer care guidelines. The Node classification was performed using a Support Vector Machine (SVM) model, achieving a classification accuracy of 0.81 with 10-fold cross-validation

Waste and the built living environment: Municipal and socio-cultural systems in Finland

Media files notes: Stakeholder, systems and mind maps. Description: These are maps that I made during the process of the thesis. Media rights: CC-BY-NC-ND 4.

Qualitative Analysis of Tree Canopy Top Points Extraction from Different Terrestrial Laser Scanner Combinations in Forest Plots

In forestry research, for forest inventories or other applications which require accurate 3D information on the forest structure, a Terrestrial Laser Scanner (TLS) is an efficient tool for vegetation structure estimation. Light Detection and Ranging (LiDAR) can even provide high-resolution information in tree canopies due to its high penetration capability. Depending on the forest plot size, tree density, and structure, multiple TLS scans are acquired to cover the forest plot in all directions to avoid any voids in the dataset that are generated. However, while increasing the number of scans, we often tend to increase the data redundancy as we keep acquiring data for the same region from multiple scan positions. In this research, an extensive qualitative analysis was carried out to examine the capability and efficiency of TLS to generate canopy top points in six different scanning combinations. A total of nine scans were acquired for each forest plot, and from these nine scans, we made six different combinations to evaluate the 3D vegetation structure derived from each scan combination, such as Center Scans (CS), Four Corners Scans (FCS), Four Corners with Center Scans (FCwCS), Four Sides Center Scans (FSCS), Four Sides Center with Center Scans (FSCwCS), and All Nine Scans (ANS). We considered eight forest plots with dimensions of 25 m × 25 m, of which four plots were of medium tree density, and the other four had a high tree density. The forest plots are located in central Slovakia; European beech was the dominant tree species with a mixture of European oak, Silver fir, Norway spruce, and European hornbeam. Altogether, 487 trees were considered for this research. The quantification of tree canopy top points obtained from a TLS point cloud is very crucial as the point cloud is used to derive the Digital Surface Model (DSM) and Canopy Height Model (CHM). We also performed a statistical evaluation by calculating the differences in the canopy top points between ANS and the five other combinations and found that the most significantly different combination was FSCwCS respective to ANS. The Root Mean Squared Error (RMSE) of the deviations in tree canopy top points obtained for plots TLS_Plot1 and TLS_Plot2 ranged from 0.89 m to 14.98 m and 0.61 m to 7.78 m, respectively. The relative Root Mean Squared Error (rRMSE) obtained for plots TLS_Plot1 and TLS_Plot2 ranged from 0.15% to 2.48% and 0.096% to 1.22%, respectively

Association between social vulnerability index and cardiovascular disease: A behavioral risk factor surveillance system study

Background Social and environmental factors play an important role in the rising health care burden of cardiovascular disease. The Centers for Disease Control and Prevention developed the Social Vulnerability Index (SVI) from US census data as a tool for public health officials to identify communities in need of support in the setting of a hazardous event. SVI (ranging from a least vulnerable score of 0 to a most vulnerable score of 1) ranks communities on 15 social factors including unemployment, minoritized groups status, and disability, and groups them under 4 broad themes: socioeconomic status, housing and transportation, minoritized groups, and household composition. We sought to assess the association of SVI with self-reported prevalent cardiovascular comorbidities and atherosclerotic cardiovascular disease (ASCVD). Methods and Results We performed a retrospective cohort analysis of adults (≥18 years) in the Behavioral Risk Factor Surveillance System 2016 to 2019. Data regarding self-reported prevalent cardiovascular comorbidities (including diabetes, hypertension, hyperlipidemia, smoking, substance use), and ASCVD was captured using participants\u27 response to a structured telephonic interview. We divided states on the basis of the tertile of SVI (first-participant lives in the least vulnerable group of states, 0-0.32; to third-participant lives in the most vulnerable group of states, 0.54-1.0). Multivariable logistic regression models adjusting for age, race and ethnicity, sex, employment, income, health care coverage, and association with federal poverty line were constructed to assess the association of SVI with cardiovascular comorbidities. Our study sample consisted of 1 745 999 participants ≥18 years of age. States in the highest (third) tertile of social vulnerability had predominantly Black and Hispanic adults, lower levels of education, lower income, higher rates of unemployment, and higher rates of prevalent comorbidities including hypertension, diabetes, chronic kidney disease, hyperlipidemia, substance use, and ASCVD. In multivariable logistic regression models, individuals living in states in the third tertile of SVI had higher odds of having hypertension (odds ratio (OR), 1.14 [95% CI, 1.11-1.17]), diabetes (OR, 1.12 [95% CI, 1.09-1.15]), hyperlipidemia (OR, 1.09 [95% CI, 1.06-1.12]), chronic kidney disease (OR, 1.17 [95% CI, 1.12-1.23]), smoking (OR, 1.05 [95% CI, 1.03-1.07]), and ASCVD (OR, 1.15 [95% CI, 1.12-1.19]), compared with those living in the first tertile of SVI. Conclusions SVI varies across the US states and is associated with prevalent cardiovascular comorbidities and ASCVD, independent of age, race and ethnicity, sex, employment, income, and health care coverage. SVI may be a useful assessment tool for health policy makers and health systems researchers examining multilevel influences on cardiovascular-related health behaviors and identifying communities for targeted interventions pertaining to social determinants of health

Association Between Social Vulnerability Index and Cardiovascular Disease: A Behavioral Risk Factor Surveillance System Study

Background Social and environmental factors play an important role in the rising health care burden of cardiovascular disease. The Centers for Disease Control and Prevention developed the Social Vulnerability Index (SVI) from US census data as a tool for public health officials to identify communities in need of support in the setting of a hazardous event. SVI (ranging from a least vulnerable score of 0 to a most vulnerable score of 1) ranks communities on 15 social factors including unemployment, minoritized groups status, and disability, and groups them under 4 broad themes: socioeconomic status, housing and transportation, minoritized groups, and household composition. We sought to assess the association of SVI with self-reported prevalent cardiovascular comorbidities and atherosclerotic cardiovascular disease (ASCVD). Methods and Results We performed a retrospective cohort analysis of adults (≥18 years) in the Behavioral Risk Factor Surveillance System 2016 to 2019. Data regarding self-reported prevalent cardiovascular comorbidities (including diabetes, hypertension, hyperlipidemia, smoking, substance use), and ASCVD was captured using participants\u27 response to a structured telephonic interview. We divided states on the basis of the tertile of SVI (first-participant lives in the least vulnerable group of states, 0-0.32; to third-participant lives in the most vulnerable group of states, 0.54-1.0). Multivariable logistic regression models adjusting for age, race and ethnicity, sex, employment, income, health care coverage, and association with federal poverty line were constructed to assess the association of SVI with cardiovascular comorbidities. Our study sample consisted of 1 745 999 participants ≥18 years of age. States in the highest (third) tertile of social vulnerability had predominantly Black and Hispanic adults, lower levels of education, lower income, higher rates of unemployment, and higher rates of prevalent comorbidities including hypertension, diabetes, chronic kidney disease, hyperlipidemia, substance use, and ASCVD. In multivariable logistic regression models, individuals living in states in the third tertile of SVI had higher odds of having hypertension (odds ratio (OR), 1.14 [95% CI, 1.11-1.17]), diabetes (OR, 1.12 [95% CI, 1.09-1.15]), hyperlipidemia (OR, 1.09 [95% CI, 1.06-1.12]), chronic kidney disease (OR, 1.17 [95% CI, 1.12-1.23]), smoking (OR, 1.05 [95% CI, 1.03-1.07]), and ASCVD (OR, 1.15 [95% CI, 1.12-1.19]), compared with those living in the first tertile of SVI. Conclusions SVI varies across the US states and is associated with prevalent cardiovascular comorbidities and ASCVD, independent of age, race and ethnicity, sex, employment, income, and health care coverage. SVI may be a useful assessment tool for health policy makers and health systems researchers examining multilevel influences on cardiovascular-related health behaviors and identifying communities for targeted interventions pertaining to social determinants of health

Association Between Social Vulnerability Index and Cardiovascular Disease: A Behavioral Risk Factor Surveillance System Study

Background Social and environmental factors play an important role in the rising health care burden of cardiovascular disease. The Centers for Disease Control and Prevention developed the Social Vulnerability Index (SVI) from US census data as a tool for public health officials to identify communities in need of support in the setting of a hazardous event. SVI (ranging from a least vulnerable score of 0 to a most vulnerable score of 1) ranks communities on 15 social factors including unemployment, minoritized groups status, and disability, and groups them under 4 broad themes: socioeconomic status, housing and transportation, minoritized groups, and household composition. We sought to assess the association of SVI with self‐reported prevalent cardiovascular comorbidities and atherosclerotic cardiovascular disease (ASCVD). Methods and Results We performed a retrospective cohort analysis of adults (≥18 years) in the Behavioral Risk Factor Surveillance System 2016 to 2019. Data regarding self‐reported prevalent cardiovascular comorbidities (including diabetes, hypertension, hyperlipidemia, smoking, substance use), and ASCVD was captured using participants' response to a structured telephonic interview. We divided states on the basis of the tertile of SVI (first—participant lives in the least vulnerable group of states, 0–0.32; to third—participant lives in the most vulnerable group of states, 0.54–1.0). Multivariable logistic regression models adjusting for age, race and ethnicity, sex, employment, income, health care coverage, and association with federal poverty line were constructed to assess the association of SVI with cardiovascular comorbidities. Our study sample consisted of 1 745 999 participants ≥18 years of age. States in the highest (third) tertile of social vulnerability had predominantly Black and Hispanic adults, lower levels of education, lower income, higher rates of unemployment, and higher rates of prevalent comorbidities including hypertension, diabetes, chronic kidney disease, hyperlipidemia, substance use, and ASCVD. In multivariable logistic regression models, individuals living in states in the third tertile of SVI had higher odds of having hypertension (odds ratio (OR), 1.14 [95% CI, 1.11–1.17]), diabetes (OR, 1.12 [95% CI, 1.09–1.15]), hyperlipidemia (OR, 1.09 [95% CI, 1.06–1.12]), chronic kidney disease (OR, 1.17 [95% CI, 1.12–1.23]), smoking (OR, 1.05 [95% CI, 1.03–1.07]), and ASCVD (OR, 1.15 [95% CI, 1.12–1.19]), compared with those living in the first tertile of SVI. Conclusions SVI varies across the US states and is associated with prevalent cardiovascular comorbidities and ASCVD, independent of age, race and ethnicity, sex, employment, income, and health care coverage. SVI may be a useful assessment tool for health policy makers and health systems researchers examining multilevel influences on cardiovascular‐related health behaviors and identifying communities for targeted interventions pertaining to social determinants of health