Practicing Diagnostic Justice

Via an analysis of the coronavirus disease 2019 pandemic, this chapter addresses the various uses of diagnostic testing that go beyond clinical patient care, such as the promotion of public health (via either prevention strategies or research studies). Furthermore, it addresses the question of what to do when diagnostic tests are scarce: For the physician, testing allocations should be made, in the first instance, on the basis of the needs of the individual patient, and societal concerns should be considered to be secondary. For a medical researcher, on the other hand, the priority is reversed: When acquisition of knowledge is the primary goal, considerations of individual patients and their care will necessarily be secondary.

Brain Tumor Discussions on Twitter (#BTSM): Social Network Analysis

Background The Brain Tumor Social Media (#BTSM) Twitter hashtag was founded in February 2012 as a disease-specific hashtag for patients with brain tumor. Objective To understand #BTSM’s role as a patient support system, we describe user descriptors, growth, interaction, and content sharing. Methods We analyzed all tweets containing #BTSM from 2012 to 2018 using the Symplur Signals platform to obtain data and to describe Symplur-defined user categories, tweet content, and trends in use over time. We created a network plot with all publicly available retweets involving #BTSM in 2018 to visualize key stakeholders and their connections to other users. Results From 2012 to 2018, 59,764 unique users participated in #BTSM, amassing 298,904 tweets. The yearly volume of #BTSM tweets increased by 264.57% from 16,394 in 2012 to 43,373 in 2018 with #BTSM constantly trending in the top 15 list of disease hashtags, as well the top 15 list of tweet chats. Patient advocates generated the most #BTSM tweets (33.13%), while advocacy groups, caregivers, doctors, and researchers generated 7.01%, 4.63%, 3.86%, and 3.37%, respectively. Physician use, although still low, has increased over time. The 2018 network plot of retweets including #BTSM identifies a number of key stakeholders from the patient advocate, patient organization, and medical researcher domains and reveals the extent of their reach to other users. Conclusions From its start in 2012, #BTSM has grown exponentially over time. We believe its growth suggests its potential as a global source of brain tumor information on Twitter for patients, advocates, patient organizations as well as health care professionals and researchers.

Brain Tumor Discussions on Twitter (#BTSM): Social Network Analysis (Preprint)

BACKGROUND The Brain Tumor Social Media (#BTSM) Twitter hashtag was founded in February 2012 as a disease-specific hashtag for patients with brain tumor. OBJECTIVE To understand #BTSM’s role as a patient support system, we describe user descriptors, growth, interaction, and content sharing. METHODS We analyzed all tweets containing #BTSM from 2012 to 2018 using the Symplur Signals platform to obtain data and to describe Symplur-defined user categories, tweet content, and trends in use over time. We created a network plot with all publicly available retweets involving #BTSM in 2018 to visualize key stakeholders and their connections to other users. RESULTS From 2012 to 2018, 59,764 unique users participated in #BTSM, amassing 298,904 tweets. The yearly volume of #BTSM tweets increased by 264.57% from 16,394 in 2012 to 43,373 in 2018 with #BTSM constantly trending in the top 15 list of disease hashtags, as well the top 15 list of tweet chats. Patient advocates generated the most #BTSM tweets (33.13%), while advocacy groups, caregivers, doctors, and researchers generated 7.01%, 4.63%, 3.86%, and 3.37%, respectively. Physician use, although still low, has increased over time. The 2018 network plot of retweets including #BTSM identifies a number of key stakeholders from the patient advocate, patient organization, and medical researcher domains and reveals the extent of their reach to other users. CONCLUSIONS From its start in 2012, #BTSM has grown exponentially over time. We believe its growth suggests its potential as a global source of brain tumor information on Twitter for patients, advocates, patient organizations as well as health care professionals and researchers.

Statistical analysis and estimation of clinical effect based on Bayes factor for otorhinolaryngology

The article discusses the use of Bayesian methods for statistical inference as an alternative to the traditional method of testing hypotheses based on the significance level. Illustrative examples of solving traditional statistical problems of hypothesis testing in otorhinolaryngology based on the calculation and interpretation of the Bayes factor are presented. As two tasks for illustration, we used the tasks of assessing the impact of sauna visits on indicators of nasal flow in patients with allergic rhinitis and the task of assessing the association of the season of the year and the frequency of birth of children with hearing impairment. Although in the form of an article it is impossible to fully describe and explain all the mathematical terms and their origin for understanding the logic of Bayesian methods, we tried to explain what they mean without reference to the mathematical manuals. As Bayesian methods are increasingly used in statistical applications, a basic understanding of how to calculate them should be part of the toolkit of every medical researcher, and how to interpret it, of every practitioner who is interested in modern results of clinical trials. All calculations used in the article are accompanied by an R-code, so they can easily be reproduced, the text of the article can be used as step-by-step instructions for their implementation.

EPID-30. A TWITTER-BASED NETWORK ANALYSIS OF BRAIN TUMOR SOCIAL MEDIA (#BTSM)

BACKGROUND The Brain Tumor Social Media (#BTSM) Twitter hashtag was founded in February 2012 as a disease-specific hashtag for brain tumor patients. To understand #BTSM’s role as a patient support system we describe user descriptors, growth, interaction, and content sharing. METHODS We analyzed all tweets containing #BTSM from 2012 to 2018 using the Symplur Signals platform to obtain data and to describe Symplur-defined user categories, tweet content, and trends in use over time. We created a network plot with all publicly-available retweets involving #BTSM in 2018 to visualize key stakeholders and their connections to other users. RESULTS From 2012 to 2018, 59764 unique users participated in #BTSM, amassing 298904 tweets. The yearly volume of #BTSM tweets increased by 264% from 2012 to 2018 with #BTSM constantly trending in the top 15 list of disease hashtags, as well the top 15 list of tweet chats. Patient advocates generated the most #BTSM tweets (33.0%) while advocacy groups, researchers, caregivers and doctors, generated 28.8%, 7.0%, 4.6% and 3.9%, respectively. Physician use, although still low, has increased over time. The 2018 network plot of retweets including #BTSM identifies a number of key stakeholders from the patient advocate, patient organization, and medical researcher domains and reveals the extent of their reach to other users. CONCLUSIONS From its start in 2012, #BTSM has grown exponentially over time. We believe its growth suggests its potential as a global source of brain tumor information for patients, advocates, patient organizations as well as healthcare professionals and researchers.

Logistic Regression for Health Profiling

in an event when there is lots of risk factor then the logistic regression is used for predicting the probability. For binary and ordinal data the medical researcher increase the use of logistic analysis. Several classification problems like spam detection used logistic regression. If a customer purchases a specific product in Diabetes prediction or they will inspire with any other competitor, whether customer click on given advertisement link or not are some example. For two class classification the Logistic Regression is one of the most simple and common machine Learning algorithms. For any binary classification problem it is very easy to use as a basic approach. Deep learning is also its fundamental concept. The relationship measurement and description between dependent binary variable and independent variables can be done by logistic regression.

Blonde Venus

This chapter examines Blonde Venus (1932), Sternberg and Dietrich’s characteristically atypical take on the fallen woman film genre. Dietrich’s character is as much liberated as cast out from the family home when she resumes her earlier career in show business and is condemned by her husband for prostitution. Yet the downward trajectory of the fallen woman genre never really exerts its grip on Dietrich, for she remains a mythical being. The chapter interprets the film as a critique of the patriarchal institution of marriage in which standards are expected of the woman that are not expected of the man: Dietrich’s character’s husband shuns her for selling her body, even though he attempts to sell his own (to a medical researcher). The question of the film that the chapter explores is the reconcilability of fairy-tale romance and everyday marriage: Blonde Venus does not take for granted the transition from the one to the other.