SUMMARY BackgroundSeveral adipocytokines have been implicated in the pathogenesis nonalcoholic fatty liver disease (NAFLD).

OBJECTIVE:Crowdsourcing research allows investigators to engage thousands of people to provide either data or data analysis. However, prior work has not documented the use of crowdsourcing in health and medical research. We sought to systematically review the literature to describe the scope of crowdsourcing in health research and to create a taxonomy to characterize past uses of this methodology for health and medical research. DATA SOURCES: PubMed, Embase, and CINAHL through March 2013. STUDY ELIGIBILITY CRITERIA: Primary peerreviewed literature that used crowdsourcing for health research. STUDY APPRAISAL AND SYNTHESIS METHODS: Two authors independently screened studies and abstracted data, including demographics of the crowd engaged and approaches to crowdsourcing. RESULTS: Twenty-one health-related studies utilizing crowdsourcing met eligibility criteria. Four distinct types of crowdsourcing tasks were identified: problem solving, data processing, surveillance/monitoring, and surveying. These studies collectively engaged a crowd of >136,395 people, yet few studies reported demographics of the crowd. Only one (5 %) reported age, sex, and race statistics, and seven (33 %) reported at least one of these descriptors. Most reports included data on crowdsourcing logistics such as the length of crowdsourcing (n=18, 86 %) and time to complete crowdsourcing task (n=15, 71 %). All articles (n=21, 100 %) reported employing some method for validating or improving the quality of data reported from the crowd. LIMITATIONS: Gray literature not searched and only a sample of online survey articles included. CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS:Utilizing crowdsourcing can improve the quality, cost, and speed of a research project while engaging large segments of the public and creating novel science. Standardized guidelines are needed on crowdsourcing metrics that should be collected and reported to provide clarity and comparability in methods. INTRODUCTIONCrowdsourcing is an approach to accomplishing a task by opening up its completion to broad sections of the public. Innovation tournaments, prizes for solving an engineering problem, or paying online participants for categorizing images are examples of crowdsourcing. What ties these approaches together is that the task is outsourced with little restriction on who might participate. Despite the potential of crowdsourcing, little is known about the applications and feasibility of this approach for collecting or analyzing health and medical research data where the stakes are high for data quality and validity.One of the most celebrated crowdsourcing tasks was the prize established in 1714 by Britain's Parliament in the Longitude Act, offered to anyone who could solve the problem of identifying a ship's longitudinal position.1 The Audubon Society's Christmas Bird Count began in 1900 and continues to this day as a way for "citizen scientists" to provide data that can be used for studying bird population trends.2 However, today the world has 2.3 billion Internet users an...

2.Malik AO, Spertus JA, Patel MR, et al. Potential association of the ISCHEMIA trial with the appropriate use criteria ratings for percutaneous coronary intervention in stable ischemic heart disease. JAMA Intern Med.

Little is known about how real-time online rating platforms such as Yelp may complement the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey, the U.S. standard for evaluating patient experiences after hospitalization. We compared the content of Yelp narrative reviews of hospitals to the domains covered by HCAHPS. While the domains included in Yelp reviews covered the majority of HCAHPS domains, Yelp reviews covered an additional twelve domains not reflected in HCAHPS. The majority of Yelp topics most strongly correlated with positive or negative reviews are not measured or reported by HCAHPS. Yelp provides a large collection of patient and caregiver-centered experiences that can be analyzed with natural language processing methods to identify for policy makers what measures of hospital quality matter most to patients and caregivers while also providing actionable feedback for hospitals.

BackgroundPatients have adopted web-based tools to report on the quality of their healthcare experiences. We seek to examine online reviews for US emergency departments (EDs) posted on Yelp, a popular consumer ratings website.MethodsWe conducted a qualitative analysis of unstructured, publicly accessible reviews for hospitals available onhttp://www.yelp.com. We collected all reviews describing experiences of ED care for a stratified random sample of 100 US hospitals. We analysed the content of the reviews using themes derived from the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) inpatient care survey. We also used modified grounded theory to iteratively code the text of the reviews, identifying additional themes specific to emergency care. The data were double-coded, and discrepancies were evaluated to ensure consensus.ResultsOf the 1736 total reviews, 573 (33%) described patient experiences involving the ED. The reviews contained several themes assessed by the HCAHPS survey, including communication with nurses, communication with doctors, and pain control. The reviews also contained key themes specific to emergency care: waiting and efficiency; decisions to seek care in the ED; and events following discharge, including administrative difficulties.ConclusionsThese exploratory findings suggest that online reviews for EDs contain similar themes to survey-based assessments of inpatient hospital care as well as themes specific to emergency care. Consumer rating websites allow patients to provide rapid and public feedback on their experience of medical care. Web-based platforms may offer a novel strategy for assessing patient-centred quality in emergency care.

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COVID-19 presentations range from mild to moderate through severe disease but also manifest with persistent illness or viral recrudescence. We hypothesized that the spectrum of COVID-19 disease manifestations was a consequence of SARS-CoV-2-mediated delay in the pathogen-associated molecular pattern (PAMP) response, including dampened type I interferon signaling, thereby shifting the balance of the immune response to be dominated by damage-associated molecular pattern (DAMP) signaling. To test the hypothesis, we constructed a parsimonious mechanistic mathematical model. After calibration of the model for initial viral load and then by varying a few key parameters, we show that the core model generates four distinct viral load, immune response and associated disease trajectories termed “patient archetypes”, whose temporal dynamics are reflected in clinical data from hospitalized COVID-19 patients. The model also accounts for responses to corticosteroid therapy and predicts that vaccine-induced neutralizing antibodies and cellular memory will be protective, including from severe COVID-19 disease. This generalizable modeling framework could be used to analyze protective and pathogenic immune responses to diverse viral infections.

Background: Characterization of coronavirus disease 2019 (COVID-19) endotypes may help explain variable clinical presentations and response to treatments. While risk factors for COVID-19 have been described, COVID-19 endotypes have not been elucidated.Objectives: We sought to identify and describe COVID-19 endotypes of hospitalized patients.Methods: Consensus clustering (using the ensemble method) of patient age and laboratory values during admission identified endotypes. We analyzed data from 528 patients with COVID-19 who were admitted to telemetry capable beds at Columbia University Irving Medical Center and discharged between March 12 to July 15, 2020.Results: Four unique endotypes were identified and described by laboratory values, demographics, outcomes, and treatments. Endotypes 1 and 2 were comprised of low numbers of intubated patients (1 and 6%) and exhibited low mortality (1 and 6%), whereas endotypes 3 and 4 included high numbers of intubated patients (72 and 85%) with elevated mortality (21 and 43%). Endotypes 2 and 4 had the most comorbidities. Endotype 1 patients had low levels of inflammatory markers (ferritin, IL-6, CRP, LDH), low infectious markers (WBC, procalcitonin), and low degree of coagulopathy (PTT, PT), while endotype 4 had higher levels of those markers.Conclusions: Four unique endotypes of hospitalized patients with COVID-19 were identified, which segregated patients based on inflammatory markers, infectious markers, evidence of end-organ dysfunction, comorbidities, and outcomes. High comorbidities did not associate with poor outcome endotypes. Further work is needed to validate these endotypes in other cohorts and to study endotype differences to treatment responses.