CamChoice: A Corpus of Multiple Choice Questions and Candidate Response Distributions

Multiple Choice examinations are a ubiquitous form of assessment that is used to measure the ability of candidates across various domains and tasks. Maintaining the quality of proposed questions is of great importance to test designers, and therefore newly proposed questions go through several pre-test evaluation stages before they can be deployed into real-world exams. This process is currently quite manual, which can lead to time lags in the question development cycle. Automating this process would lead to a large improvement in efficiency, however, current datasets do not contain sufficient pre-test analysis information. In this paper, we introduce CamChoice; a multiple-choice comprehension dataset with questions at different target levels, where questions have the true candidate selected options distributions. We introduce the task of candidate distribution matching, propose several evaluation metrics for the task, and demonstrate that automatic systems trained on RACE++ can be leveraged as baselines for our task. We further demonstrate that these automatic systems can be used for practical pre-test evaluation tasks such as detecting underperforming distractors, where our detection systems can automatically identify poor distractors that few candidates select. We release the data publicly for future research.Comment: 9 pages, 6 figures, 7 table

Four different study designs to evaluate vaccine safety were equally validated with contrasting limitations

OBJECTIVE: We conducted a simulation study to empirically compare four study designs [cohort, case-control, risk-interval, self-controlled case series (SCCS)] used to assess vaccine safety. STUDY DESIGN AND METHODS: Using Vaccine Safety Datalink data (a Centers for Disease Control and Prevention-funded project), we simulated 250 case sets of an acute illness within a cohort of vaccinated and unvaccinated children. We constructed the other three study designs from the cohort at three different incident rate ratios (IRRs, 2.00, 3.00, and 4.00), 15 levels of decreasing disease incidence, and two confounding levels (20%, 40%) for both fixed and seasonal confounding. Each of the design-specific study samples was analyzed with a regression model. The design-specific beta; estimates were compared. RESULTS: The beta; estimates of the case-control, risk-interval, and SCCS designs were within 5% of the true risk parameters or cohort estimates. However, the case-control\u27s estimates were less precise, less powerful, and biased by fixed confounding. The estimates of SCCS and risk-interval designs were biased by unadjusted seasonal confounding. CONCLUSIONS: All the methods were valid designs, with contrasting strengths and weaknesses. In particular, the SCCS method proved to be an efficient and valid alternative to the cohort method

CGRP, adrenomedullin and adrenomedullin 2 display endogenous GPCR agonist bias in primary human cardiovascular cells.

Agonist bias occurs when different ligands produce distinct signalling outputs when acting at the same receptor. However, its physiological relevance is not always clear. Using primary human cells and gene editing techniques, we demonstrate endogenous agonist bias with physiological consequences for the calcitonin receptor-like receptor, CLR. By switching the receptor-activity modifying protein (RAMP) associated with CLR we can "re-route" the physiological pathways activated by endogenous agonists calcitonin gene-related peptide (CGRP), adrenomedullin (AM) and adrenomedullin 2 (AM2). AM2 promotes calcium-mediated nitric oxide signalling whereas CGRP and AM show pro-proliferative effects in cardiovascular cells, thus providing a rationale for the expression of the three peptides. CLR-based agonist bias occurs naturally in human cells and has a fundamental purpose for its existence. We anticipate this will be a starting point for more studies into RAMP function in native environments and their importance in endogenous GPCR signalling

Epidemiologic Risk Factors for In Situ and Invasive Breast Cancers Among Postmenopausal Women in the National Institutes of Health-AARP Diet and Health Study

Comparing risk factor associations between invasive breast cancers and possible precursors may further our understanding of factors related to initiation versus progression. Accordingly, among 190,325 postmenopausal participants in the National Institutes of Health-AARP Diet and Health Study (1995-2011), we compared the association between risk factors and incident ductal carcinoma in situ (DCIS; n = 1,453) with that of risk factors and invasive ductal carcinomas (n = 7,525); in addition, we compared the association between risk factors and lobular carcinoma in situ (LCIS; n = 186) with that of risk factors and invasive lobular carcinomas (n = 1,191). Hazard ratios and 95% confidence intervals were estimated from multivariable Cox proportional hazards regression models. We used case-only multivariable logistic regression to test for heterogeneity in associations. Younger age at menopause was associated with a higher risk of DCIS but lower risks of LCIS and invasive ductal carcinomas (P for heterogeneity < 0.01). Prior breast biopsy was more strongly associated with the risk of LCIS than the risk of DCIS (P for heterogeneity = 0.04). Increased risks associated with use of menopausal hormone therapy were stronger for LCIS than DCIS (P for heterogeneity = 0.03) and invasive lobular carcinomas (P for heterogeneity < 0.01). Associations were similar for race, age at menarche, age at first birth, family history, alcohol consumption, and smoking status, which suggests that most risk factor associations are similar for in situ and invasive cancers and may influence early stages of tumorigenesis. The differential associations observed for various factors may provide important clues for understanding the etiology of certain breast cancers

Measuring the Quality of Data Collection in a Large Observational Cohort of HIV and AIDS

The aim of this study was to examine the quality of data collection by studying the validity of collected data. Data were extracted from the clinic charts of two anonymous outpatients by 38 data collectors. A standard for the data to be collected was determined (168 items). The validity was measured by comparing the collected items with the standard; in this way, the percentages of the collected items that were ‘correct’ could be calculated. The percentage ‘correct’ was higher for clinic chart 1 (mean: 83% correct, SD 7%) than for clinic chart 2 (mean: 78% correct, SD 8%). All categories contained incorrectly collected data. These data were divided into missing data, incorrect start-stop dates, and surplus collected data. Almost all start-stop dates would change into ‘correct’ if ‘monthyear’ was considered correct (instead of the standard ‘daymonthyear’). Not all data collectors used specific protocols, and sources other than the written comments were not always checked. This study shows that a high proportion of data was correctly collected. However, the collection of start-stop dates was not optimal, and the collected data included surplus and missing data. Data collectors should be more knowledgeable about HIV disease and trained in the use of difficult protocols, so that they can better recognize what data to collect and how it should be collected. Among physicians, there should be more agreement about what information to record in the charts, to facilitate data extraction for data collectors

An analysis of national target groups for monovalent 2009 pandemic influenza vaccine and trivalent seasonal influenza vaccines in 2009-10 and 2010-11

<p>Abstract</p> <p>Background</p> <p>Vaccination is generally considered to be the best primary prevention measure against influenza virus infection. Many countries encourage specific target groups of people to undertake vaccination, often with financial subsidies or a priority list. To understand differential patterns of national target groups for influenza vaccination before, during and after the 2009 influenza pandemic, we reviewed and analyzed the country-specific policies in the corresponding time periods.</p> <p>Methods</p> <p>Information on prioritized groups targeted to receive seasonal and pandemic influenza vaccines was derived from a multi-step internet search of official health department websites, press releases, media sources and academic journal articles. We assessed the frequency and consistency of targeting 20 different groups within populations which are associated with age, underlying medical conditions, role or occupations among different countries and vaccines. Information on subsidies provided to specific target groups was also extracted.</p> <p>Results</p> <p>We analyzed target groups for 33 (seasonal 2009 and 2009-10 vaccines), 72 (monovalent pandemic 2009-10 vaccine) and 34 (seasonal 2010 and 2010-11 vaccines) countries. In 2009-10, the elderly, those with chronic illness and health care workers were common targets for the seasonal vaccine. Comparatively, the elderly, care home residents and workers, animal contacts and close contacts were less frequently targeted to receive the pandemic vaccine. Pregnant women, obese persons, essential community workers and health care workers, however, were more commonly targeted. After the pandemic, pregnant women, obese persons, health care and care home workers, and close contacts were more commonly targeted to receive the seasonal vaccine compared to 2009-10, showing continued influence from the pandemic. Many of the countries provided free vaccines, partial subsidies, reimbursements or national health insurance coverage to specific target groups and over one-third of the countries offered universal subsidy regarding the pandemic vaccine. There was also some inconsistency between countries in target groups.</p> <p>Conclusions</p> <p>Differences in target groups between countries may reflect variable objectives as well as uncertainties regarding the transmission dynamics, severity and age-specific immunity against influenza viruses before and after vaccination. Clarification on these points is essential to elucidate optimal and object-oriented vaccination strategies.</p

Estimation of the national disease burden of influenza-associated severe acute respiratory illness in Kenya and Guatemala : a novel methodology

Background: Knowing the national disease burden of severe influenza in low-income countries can inform policy decisions around influenza treatment and prevention. We present a novel methodology using locally generated data for estimating this burden. Methods and Findings: This method begins with calculating the hospitalized severe acute respiratory illness (SARI) incidence for children <5 years old and persons ≥5 years old from population-based surveillance in one province. This base rate of SARI is then adjusted for each province based on the prevalence of risk factors and healthcare-seeking behavior. The percentage of SARI with influenza virus detected is determined from provincial-level sentinel surveillance and applied to the adjusted provincial rates of hospitalized SARI. Healthcare-seeking data from healthcare utilization surveys is used to estimate non-hospitalized influenza-associated SARI. Rates of hospitalized and non-hospitalized influenza-associated SARI are applied to census data to calculate the national number of cases. The method was field-tested in Kenya, and validated in Guatemala, using data from August 2009–July 2011. In Kenya (2009 population 38.6 million persons), the annual number of hospitalized influenza-associated SARI cases ranged from 17,129–27,659 for children <5 years old (2.9–4.7 per 1,000 persons) and 6,882–7,836 for persons ≥5 years old (0.21–0.24 per 1,000 persons), depending on year and base rate used. In Guatemala (2011 population 14.7 million persons), the annual number of hospitalized cases of influenza-associated pneumonia ranged from 1,065–2,259 (0.5–1.0 per 1,000 persons) among children <5 years old and 779–2,252 cases (0.1–0.2 per 1,000 persons) for persons ≥5 years old, depending on year and base rate used. In both countries, the number of non-hospitalized influenza-associated cases was several-fold higher than the hospitalized cases. Conclusions: Influenza virus was associated with a substantial amount of severe disease in Kenya and Guatemala. This method can be performed in most low and lower-middle income countries

Self-reported adverse reactions in 4337 healthcare workers immunizations against novel H1N1 influenza

Purpose: The use of the 2009 H1N1 vaccine has generated much debate concerning safety issues among the general population and physicians. Therefore, we investigated the safety of an inactivated monovalent H1N1 pandemic influenza vaccine Methods: We focused on the H1N1 pandemic influenza vaccine Pandemrix(R) and applied a self reporting questionnaire in a population of healthcare workers (HCWs) and medical students at a major university hospital. Results: In total, 4337 individuals were vaccinated, consisting of 3808 HCWs and 529 medical students. The vaccination rate of the employees was higher than 40%.The majority of individuals were vaccinated in November 2009. In total, 291 of the 4337 vaccinations were reported to lead to one or more adverse reactions (6.7%). Local reactions were reported in 3.8%, myalgia and arthralgia in 3.7%, fatigue in 3.7%, headache in 3.1%. Conclusions: Our data together with available data from several national and international institutions points to a safe pandemic influenza vaccine

Double–blind control of the data manager doesn't have any impact on data entry reliability and should be considered as an avoidable cost

<p>Abstract</p> <p>Background</p> <p>Database systems have been developed to store data from large medical trials and survey studies. However, a reliable data storage system does not guarantee data entering reliability.</p> <p>We aimed to evaluate if double-blind control of the data manager might have any effect on data-reliability. Our secondary aim was to assess the influence of the inserting position in the insertion-sheet on data-entry accuracy and the effectiveness of electronic controls in identifying data-entering mistakes.</p> <p>Methods</p> <p>A cross-sectional survey and single data-manager data entry.</p> <p>Data from PACMeR_02 survey, which had been conducted within a framework of the SESy-Europe project (PACMeR_01.4), were used as substrate for this study. We analyzed the electronic storage of 6446 medical charts. We structured data insertion in four sequential phases. After each phase, the data stored in the database were tested in order to detect unreliable entries through both computerized and manual random control. Control was provided in a double blind fashion.</p> <p>Results</p> <p>Double-blind control of the data manager didn't improve data entry reliability. Entries near the end of the insertion sheet were correlated with a larger number of mistakes. Data entry monitoring by electronic-control was statistically more effective than hand-searching of randomly selected medical records.</p> <p>Conclusion</p> <p>Double-blind control of the data manager should be considered an avoidable cost. Electronic-control for monitoring of data-entry reliability is suggested.</p

Pandemic Influenza and Pregnant Women

Planning for a future influenza pandemic should include considerations specific to pregnant women. First, pregnant women are at increased risk for influenza-associated illness and death. The effects on the fetus of maternal influenza infection, associated fever, and agents used for prophylaxis and treatment should be taken into account. Pregnant women might be reluctant to comply with public health recommendations during a pandemic because of concerns regarding effects of vaccines or medications on the fetus. Guidelines regarding nonpharmaceutical interventions (e.g., voluntary quarantine) also might present special challenges because of conflicting recommendations about routine prenatal care and delivery. Finally, healthcare facilities need to develop plans to minimize exposure of pregnant women to ill persons, while ensuring that women receive necessary care