Binary Answers Research Articles

Optimal clustering from noisy binary feedback

AbstractWe study the problem of clustering a set of items from binary user feedback. Such a problem arises in crowdsourcing platforms solving large-scale labeling tasks with minimal effort put on the users. For example, in some of the recent reCAPTCHA systems, users clicks (binary answers) can be used to efficiently label images. In our inference problem, items are grouped into initially unknown non-overlapping clusters. To recover these clusters, the learner sequentially presents to users a finite list of items together with a question with a binary answer selected from a fixed finite set. For each of these items, the user provides a noisy answer whose expectation is determined by the item cluster and the question and by an item-specific parameter characterizing the hardness of classifying the item. The objective is to devise an algorithm with a minimal cluster recovery error rate. We derive problem-specific information-theoretical lower bounds on the error rate satisfied by any algorithm, for both uniform and adaptive (list, question) selection strategies. For uniform selection, we present a simple algorithm built upon the K-means algorithm and whose performance almost matches the fundamental limits. For adaptive selection, we develop an adaptive algorithm that is inspired by the derivation of the information-theoretical error lower bounds, and in turn allocates the budget in an efficient way. The algorithm learns to select items hard to cluster and relevant questions more often. We compare the performance of our algorithms with or without the adaptive selection strategy numerically and illustrate the gain achieved by being adaptive.

It takes two (seconds): decreasing encoding time for two-choice functional near-infrared spectroscopy brain-computer interface communication.

Brain-computer interfaces (BCIs) can provide severely motor-impaired patients with a motor-independent communication channel. Functional near-infrared spectroscopy (fNIRS) constitutes a promising BCI-input modality given its high mobility, safety, user comfort, cost-efficiency, and relatively low motion sensitivity. The present study aimed at developing an efficient and convenient two-choice fNIRS communication BCI by implementing a relatively short encoding time (2s), considerably increasing communication speed, and decreasing the cognitive load of BCI users. To encode binary answers to 10 biographical questions, 10 healthy adults repeatedly performed a combined motor-speech imagery task within 2 different time windows guided by auditory instructions. Each answer-encoding run consisted of 10 trials. Answers were decoded during the ongoing experiment from the time course of the individually identified most-informative fNIRS channel-by-chromophore combination. The answers of participants were decoded online with an accuracy of 85.8% (run-based group mean). Post-hoc analysis yielded an average single-trial accuracy of 68.1%. Analysis of the effect of number of trial repetitions showed that the best information-transfer rate could be obtained by combining four encoding trials. The study demonstrates that an encoding time as short as 2s can enable immediate, efficient, and convenient fNIRS-BCI communication.

Accuracy and Reliability of Chatbot Responses to Physician Questions

Natural language processing tools, such as ChatGPT (generative pretrained transformer, hereafter referred to as chatbot), have the potential to radically enhance the accessibility of medical information for health professionals and patients. Assessing the safety and efficacy of these tools in answering physician-generated questions is critical to determining their suitability in clinical settings, facilitating complex decision-making, and optimizing health care efficiency. To assess the accuracy and comprehensiveness of chatbot-generated responses to physician-developed medical queries, highlighting the reliability and limitations of artificial intelligence-generated medical information. Thirty-three physicians across 17 specialties generated 284 medical questions that they subjectively classified as easy, medium, or hard with either binary (yes or no) or descriptive answers. The physicians then graded the chatbot-generated answers to these questions for accuracy (6-point Likert scale with 1 being completely incorrect and 6 being completely correct) and completeness (3-point Likert scale, with 1 being incomplete and 3 being complete plus additional context). Scores were summarized with descriptive statistics and compared using the Mann-Whitney U test or the Kruskal-Wallis test. The study (including data analysis) was conducted from January to May 2023. Accuracy, completeness, and consistency over time and between 2 different versions (GPT-3.5 and GPT-4) of chatbot-generated medical responses. Across all questions (n = 284) generated by 33 physicians (31 faculty members and 2 recent graduates from residency or fellowship programs) across 17 specialties, the median accuracy score was 5.5 (IQR, 4.0-6.0) (between almost completely and complete correct) with a mean (SD) score of 4.8 (1.6) (between mostly and almost completely correct). The median completeness score was 3.0 (IQR, 2.0-3.0) (complete and comprehensive) with a mean (SD) score of 2.5 (0.7). For questions rated easy, medium, and hard, the median accuracy scores were 6.0 (IQR, 5.0-6.0), 5.5 (IQR, 5.0-6.0), and 5.0 (IQR, 4.0-6.0), respectively (mean [SD] scores were 5.0 [1.5], 4.7 [1.7], and 4.6 [1.6], respectively; P = .05). Accuracy scores for binary and descriptive questions were similar (median score, 6.0 [IQR, 4.0-6.0] vs 5.0 [IQR, 3.4-6.0]; mean [SD] score, 4.9 [1.6] vs 4.7 [1.6]; P = .07). Of 36 questions with scores of 1.0 to 2.0, 34 were requeried or regraded 8 to 17 days later with substantial improvement (median score 2.0 [IQR, 1.0-3.0] vs 4.0 [IQR, 2.0-5.3]; P < .01). A subset of questions, regardless of initial scores (version 3.5), were regenerated and rescored using version 4 with improvement (mean accuracy [SD] score, 5.2 [1.5] vs 5.7 [0.8]; median score, 6.0 [IQR, 5.0-6.0] for original and 6.0 [IQR, 6.0-6.0] for rescored; P = .002). In this cross-sectional study, chatbot generated largely accurate information to diverse medical queries as judged by academic physician specialists with improvement over time, although it had important limitations. Further research and model development are needed to correct inaccuracies and for validation.

Why Am I Ill? Beliefs in Supernatural and Natural Causes of Ill Health at the Time of Diagnostic Workup of Patients With Esophageal Cancer in Tanzania.

An understanding of the cultural and context-specific perceptions of the causes of cancer is an important prerequisite for designing effective primary health prevention and early detection strategies. We aimed to use the Murdock Ill Health Theoretical Model to conceptualize views on illness causation among dysphagia-suffering patients undergoing diagnostic workup for esophageal cancer (EC) in Tanzania. At the end of a structured interview on lifestyle habits, patients with suspected EC were asked about beliefs on the reasons behind their illness through (1) a set of questions with fixed binary answers, whose determinants were analyzed using logistic regression, and (2) a single question with free-text answers. Responses were coded using a hierarchy of natural and supernatural (godly and social constructs) causes. Among 322 patients interviewed between November 2015 and December 2019, we found complex and varied views about the origins of their illness. Overall, 49% of patients attributed illness to natural causes and 39% to supernatural causes. Natural causes ranged from infection, use of alcohol and tobacco, other ailments, and the environment. The supernatural causes included attributing illness to God, curses, and spells from personal acquaintances. Belief in supernatural causes was more common in the less educated and those who sought help first via a traditional healer. The results underscore the need for increased community awareness of biomedical causes of ill health and patient-based participatory research to inform prevention programs. The results also highlight the importance of building health systems that support a series of health-seeking behaviors that acknowledge both biomedical and local traditional healing belief systems.

Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation

Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only developed to provide binary answers; however, quantifying the uncertainty of the models can play a critical role for example in active learning or machine human interaction. Uncertainty quantification is especially difficult when using deep learning-based models, which are the state-of-the-art in many imaging applications. The current uncertainty quantification approaches do not scale well in high-dimensional real-world problems. Scalable solutions often rely on classical techniques, such as dropout, during inference or training ensembles of identical models with different random seeds to obtain a posterior distribution. In this paper, we present the following contributions. First, we show that the classical approaches fail to approximate the classification probability. Second, we propose a scalable and intuitive framework for uncertainty quantification in medical image segmentation that yields measurements that approximate the classification probability. Third, we suggest the usage of k-fold cross-validation to overcome the need for held out calibration data. Lastly, we motivate the adoption of our method in active learning, creating pseudo-labels to learn from unlabeled images and human–machine collaboration.

Abstract 10094: Perception of Cardiovascular Disease in Female Health Care Workers and Urgent Opportunity Gaps for Health Promotion

Introduction: Cardiovascular disease (CVD) is the largest killer in women worldwide, yet minority of GPs and less than 50% of Cardiologists feel well prepared for risk assessment in women. Women have historically been under-represented in many major CV clinical trials. The percentage of women identifying CVD as leading cause of death (LCOD) has declined from 64% in 2009 to 43% in 2019, according to surveys in the United States. In 2020, we conducted the largest single-centre survey in the United Kingdom to explore the perceptions of CVD in female healthcare workforce in North-West Anglia NHS Foundation Trust. Methods: In September 2020, a total of 620 female healthcare workers in our institute completed an online questionnaire on health concerns, LCOD in women, screening/risk factors for CVD, and perceived personal risk status. There were 21 questions containing binary, ranked and best single choice answers which took approximately 15-minutes to complete. Results: Only 51% respondents were aware of CVD as the LCOD in women and 37% correctly identified lifetime incidence of CVD as one-in-three. Sixty-five percent believed to be at risk of CVD but only 18% had CVD as their biggest health concern. Three-quarters correctly identified CVD risk factors, but 74%, 59% and 40% were not aware of their cholesterol, diabetic status and BP, respectively. CVD screening was deemed necessary aged more than 40 by 31%, and aged more than 50 by 40%. Ninety-percent had never been screened for CVD. One-in-two believed a woman was more likely to survive a heart attack at the age of 60 compared to a man of similar age. Only 12% correctly indicated that BP below 120/80mmHg was normal and 57% were not aware of effects of HRT on CVD. Lastly, 82% did not feel well informed about their CV health. Conclusions: The survey did not include baseline demographics to associate age and social/ethnical background with the responses, nevertheless, there are significant gaps in knowledge and misconception about CVD in women among female healthcare workers. This study highlights opportunities for urgent education to bring more screening programmes into practice through major stakeholders i.e. local councils, GPs and Cardiologists.

210.9: The High Incidence of Long-COVID-19 in Kidney Transplantrecipients: A Longitudinal Cohort Study

Purpose: This study aimed to investigate the clinical consequences three months after symptom onset among kidney transplant recipients surviving COVID-19. Methods: This is an ongoing single-center observational prospective study that includes adult kidney transplant recipients diagnosed and survived after COVID-19 between 03/20/2020 and 05/31/2021. Patients who lost their graft were excluded. The patients will receive a telephone at 3, 6, and 12 months after symptom onset from the clinical research team. The call consisted of a structured symptoms questionnaire with binary answers (yes or). The questionnaire included the following symptoms: headache, dizziness, anosmia/ageusia, weakness, myalgia, inappetence, diarrhea, and dyspnea, which could be presented before and/or after the COVID-19 diagnosis. Those patients with at least one symptom showed just after the disease was defined as having Long-COVID-19. Subsequently, the clinical research team included a question about the work status. Adjusted multivariable logistic regression models were used to identify the risk factors associated with Long-COVID-19. Results: There were 1,731 patients with COVID-19, with 455 deaths and 36 graft losses. Of the remaining 1,240 patients, 454 (36%) didn’t answer our calls, yielding a final cohort of 786 patients. Of them, 217 (28%) developed Long-COVID-19. The incidence of each symptom at three months was: dyspnea (7%), myalgia (12%), weakness (11%), headache (10%), dizziness (7%), diarrhea (4%), inappetence (4%) and anosmia/ageusia (3%). About 1% of our patients needed domiciliary O2. Of those who obtained the functional status (n=239), 95 (40%) were employed before COVID-19, and 79 of them (83%) had returned to their original work at three months. After COVID-19 diagnosis, 44% of the patients were hospitalized (31% in ICU), 35% used supplemental O2, and 5% required mechanical ventilation. Fever (53%), shiver (39%), nausea (3%), anosmia/ageusia (59%), hospitalization (67%), and adverse cardiovascular events (3%), such as thrombosis or myocardial infarction, were risk factors associated with subsequent development of Long-COVID-19, using adjusted multivariable logistic regression. Conclusion: The incidence of Long-COVID-19 at three months was 28% and was associated with reduced quality of life and return to work. Several COVID-19 associated symptoms and disease severity markers were associated with Long-COVID-19.

Data Science/ AI: A Possible Panacea for Refugee Crisis

Data and information are currently the two threads that intertwine to weave the shimmery fabric of modern society. We often use data and information interchangeably, but they differ considerably. For example, information analysis reveals a rationale, and logic, while data analysis, on the other hand, reveals a pattern. Moreover, patterns revealed by data can enable us to create the necessary tools to combat huge problems on our hands. They paint a clear picture so that the decision-making process becomes simple. Moreover, to solve the refugee crisis, we need to leverage that. &#x0D; &#x0D; The refugee crisis does not have binary answers. Given the emotionally wrenching nature of the ground realities, experts often shy away from realistically stating things as they are. This hesitancy can cost lives. When our decisions are based solely on data, we can remove emotions from our decision-making steps. Data also presents irrefutable evidence and tells whether there is a solution or not. Moreover, it also responds to a nonbinary crisis with a binary answer. Because of all that, it becomes easier to tackle a problem.&#x0D; &#x0D; Data science and A.I. can predict future refugee crises. With the recent explosion of data due to the rise of social media platforms, data and insight into data has solved many social and political problems. Data science can also help solve many problems refugees face while staying in refugee camps or a new country. This paper looks into various ways data science can help solve refugee problems.

Evaluating the Taste Masking Ability of Two Novel Dispersible Tablet Platforms Containing Zinc Sulfate and Paracetamol Reconstituted in a Breast Milk Substitute.

Milk is often used as a dispersion medium for medicines administration in young children but its taste-masking ability is unknown. A human taste panel was conducted to assess the potential of infant formula milk (Aptamil® 1) to mask the taste of two model WHO priority medicines, zinc sulfate and paracetamol, manufactured as dispersible tablets. Simultaneously, the palatability of powder blends of the tablet platforms was assessed. Twenty healthy adult volunteers performed a swirl-and-spit assessment of placebos and API-containing blends in either a lactose-based or a mannitol-based dispersible tablet platform, reconstituted in 10 mL of either water or Aptamil® 1. Eighteen samples were rated for aversion using a 100-mm Visual Analogue Scale, grittiness using a 5-point Likert scale, and “acceptability-as-a-medicine” evaluated as: “Would you find this sample acceptable to swallow as a medicine?” with binary answers of Yes/No. The API-containing formulations were more aversive than the placebos; the paracetamol-containing samples being more aversive than zinc sulfate samples. The platforms themselves were not aversive. Non-gritty samples had four-fold greater odds of being acceptable as a medicine. Aptamil® 1 masked the taste of zinc sulfate in the mannitol-based formulation but did not mask the taste of paracetamol in either platform, suggesting a limited taste-masking ability, which may be API and formulation dependent.

Prevalence of Physical Activity Among Healthcare Students in King Saud University, Riyadh, Saudi Arabia. An Observational Study

Background and ObjectivePhysical inactivity is 1 of the most important cardiovascular risk factors causing higher morbidity and mortality rate worldwide. Thus, the promotion of physical activity (PA) is 1 of the fundamental pillars in the primary prevention of heart disease. Therefore, this study aimed to assess the prevalence of Physical Activity among healthcare Students in the Riyadh region, Saudi Arabia.MethodsAn observational, descriptive, cross-sectional study using paper-based printed questionnaires was conducted among Emergency medical students (EMS) from the Prince Sultan College for Emergency Medical Services King Saud University. To evaluate the physical activity, Global Physical Activity Questionnaire (16-items) developed by the World health organization was used. The questionnaires talk about activity at work (6 items), travel to and from places (3-items), recreational activities (6- items), and sedentary behavior (1-item). Both binary answers (Yes\\No) and continuous scales were used to collect the data. Data were descriptively analyzed using a statistical package for social science version 26 (SPSS).ResultsThe prevalence of PA among students was 69.9% (n = 123). The most common type of physical activity was walking, accounted for 51.7% (n = 91), followed by Gym/bodybuilding 25% (n = 44), running 24.4% (n = 43). A total of 7.16 mean hours (SD = 11.23) were spent by students doing vigorous-intensity activities on a typical day, while 4.93(SD = 8.84) of mean hours were spent on moderate-intensity activities. Additionally, more than half of the students 55.7% (n = 98) performed vigorous-intensity sports, fitness, or recreational activities that cause large increases in breathing or heart rate (for at least 10 minutes continuously). The mean time spent by the students in this study performing vigorous-intensity sports, and fitness activity were 4.60(SD = 10.22) mean hours per day. The sedentary behavior, like constant sitting, was reported to be 7.33 (SD = 4.24) mean hours per day.ConclusionIn conclusion, about 3 in ten EMS students are physically inactive, which could be due to study-related actions. Future research among undergraduates should focus on approaches to increase PA is needed.

Discrimination Ability of Assessors in Check-All-That-Apply Tests: Method and Product Development.

Binary similarity measures have been used in several research fields, but their application in sensory data analysis is limited as of yet. Since check-all-that-apply (CATA) data consist of binary answers from the participants, binary similarity measures seem to be a natural choice for their evaluation. This work aims to define the discrimination ability of CATA participants by calculating the consensus values of 44 binary similarity measures. The proposed methodology consists of three steps: (i) calculating the binary similarity values of the assessors, sample pair-wise; (ii) clustering participants into good and poor discriminators based on their binary similarity values; (iii) performing correspondence analysis on the CATA data of the two clusters. Results of three case studies are presented, highlighting that a simple clustering based on the computed binary similarity measures results in higher quality correspondence analysis with more significant attributes, as well as better sample discrimination (even according to overall liking).

Achieving Approximate Global Optimization of Truth Inference for Crowdsourcing Microtasks

Microtask crowdsourcing is a form of crowdsourcing in which work is decomposed into a set of small, self-contained tasks, which each can typically be completed in a matter of minutes. Due to the various capabilities and knowledge background of the voluntary participants on the Internet, the answers collected from the crowd are ambiguous and the final answer aggregation is challenging. In this process, the choice of quality control strategies is important for ensuring the quality of the crowdsourcing results. Previous work on answer estimation mainly used expectation–maximization (EM) approach. Unfortunately, EM provides local optimal solutions and the estimated results will be affected by the initial value. In this paper, we extend the local optimal result of EM and propose an approximate global optimal algorithm for answer aggregation of crowdsourcing microtasks with binary answers. Our algorithm is expected to improve the accuracy of real answer estimation through further likelihood maximization. First, three worker quality evaluation models are presented based on static and dynamic methods, respectively, and the local optimal results are obtained based on the maximum likelihood estimation method. Then, a dominance ordering model (DOM) is proposed according to the known worker responses and worker categories for the specified crowdsourcing task to reduce the space of potential task-response sequence while retaining the dominant sequence. Subsequently, a Cut-point neighbor detection algorithm is designed to iteratively search for the approximate global optimal estimation in a reduced space, which works on the proposed dominance ordering model (DOM). We conduct extensive experiments on both simulated and real-world datasets, and the experimental results illustrate that the proposed approach can obtain better estimation results and has higher performance than regular EM-based algorithms.

Semisupervised Clustering by Queries and Locally Encodable Source Coding

Source coding is the canonical problem of data compression in information theory. In a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">locally encodable</i> source coding, each compressed bit depends on only few bits of the input. In this paper, we show that a recently popular model of semisupervised clustering is equivalent to locally encodable source coding. In this model, the task is to perform multiclass labeling of unlabeled elements. At the beginning, we can ask in parallel a set of simple queries to an oracle who provides (possibly erroneous) binary answers to the queries. The queries cannot involve more than two (or a fixed constant number of) elements. Now the labeling of all the elements (or clustering) must be performed based on the noisy query answers. The goal is to recover all the correct labelings while minimizing the number of such queries. The equivalence to locally encodable source codes leads us to find lower bounds on the number of queries required in variety of scenarios. We provide querying schemes based on pairwise ‘same cluster’ queries - and pairwise AND queries, and show provable performance guarantees for each of the schemes.

Online Discovery/Constructivistic Learning Using Cognitive Tools in Mathematics’ Higher Education

This paper analyzes online discovery learning/ constructivistic approach using cognitive tools in higher Mathematics’ education, via a combination of electronic worksheets designed and implemented in Mathematica and online synchronous communication via the tools of a Learning Management System (LMS) and voice and video group calls. Moreover, the paper presents empirical research results of a case study concerning the approach’s application at the Department of Statistics and Insurance Sciences of the University of Piraeus and focuses on students’ attitudes towards the approach. We used a mixed approach in the study, in particular a quantitative approach with open-ended questions. A questionnaire was handed out and was answered by the students that participated. We performed statistical analysis via SPSS to data obtained by questions with binary answers and answers on a 7-point Likert scale. Also we included several open-ended questions, in order for the students to express their views and attitudes towards the benefits and the disadvantages of the tools and the approach used.

Online Discovery/Constructivistic Learning Using Cognitive Tools in Mathematics’ Higher Education

This paper analyzes online discovery learning/ constructivistic approach using cognitive tools in higher Mathematics’ education, via a combination of electronic worksheets designed and implemented in Mathematica and online synchronous communication via the tools of a Learning Management System (LMS) and voice and video group calls. Moreover, the paper presents empirical research results of a case study concerning the approach’s application at the Department of Statistics and Insurance Sciences of the University of Piraeus and focuses on students’ attitudes towards the approach. We used a mixed approach in the study, in particular a quantitative approach with open-ended questions. A questionnaire was handed out and was answered by the students that participated. We performed statistical analysis via SPSS to data obtained by questions with binary answers and answers on a 7-point Likert scale. Also we included several open-ended questions, in order for the students to express their views and attitudes towards the benefits and the disadvantages of the tools and the approach used.

15. Why choose anatomical pathology? An online survey of students and doctors

Background: Globally and nationally, there has been a significant decline in the proportion of medical graduates pursuing a career in pathology, corresponding with a broadening gap between workforce supply and demand. However, there is a paucity of evidence as to the causal influences in an Australian context. Aims: To perform an online survey of Australian medical students and junior medical officers, pathology trainees and pathologists to determine the factors which attract and deter graduates from pursuing a career in anatomical pathology. Methods: Literature review was performed to identify: (1) the factors that influence the choice of speciality for medical graduates; (2) the perceptions about anatomic pathology amongst medical graduates; (3) the influence of the delivery of pathology curriculum during medical school on these perceptions and reasons; and (4) the views of pathologists and pathology trainees regarding motivations for pursuing pathology training and career satisfaction. A short online survey including questions with binary answers, response spectrums and free text was circulated to medical students (via universities), pre-vocational junior medical officers (JMOs; via JMO units), pathology registrars and pathologists (via the Royal College of Pathologists of Australasia). Results and conclusions: PubMed searches using the terms ‘pathology’, ‘training’ and ‘career choice’ identified over 200 peer reviewed indexed articles in English language; 15 pertained specifically to factors determining anatomical pathology as a career choice, and one of these was Australian. This review guided the development of specific questions related to factors identified as influencing the choice of pathology as a career (for example, personality traits, expectations regarding remuneration, research opportunities and career satisfaction, and exposure to pathology during university). The questionnaire is currently being circulated and the responses will be collated and analysed, in order to inform future recruitment strategies improving uptake to this vital but undersubscribed specialty.

Topographic Somatosensory Imagery for Real-Time fMRI Brain-Computer Interfacing.

Real-time functional magnetic resonance imaging (fMRI) is a promising non-invasive method for brain-computer interfaces (BCIs). BCIs translate brain activity into signals that allow communication with the outside world. Visual and motor imagery are often used as information-encoding strategies, but can be challenging if not grounded in recent experience in these modalities, e.g., in patients with locked-in-syndrome (LIS). In contrast, somatosensory imagery might constitute a more suitable information-encoding strategy as the somatosensory function is often very robust. Somatosensory imagery has been shown to activate the somatotopic cortex, but it has been unclear so far whether it can be reliably detected on a single-trial level and successfully classified according to specific somatosensory imagery content. Using ultra-high field 7-T fMRI, we show reliable and high-accuracy single-trial decoding of left-foot (LF) vs. right-hand (RH) somatosensory imagery. Correspondingly, higher decoding accuracies were associated with greater spatial separation of hand and foot decoding-weight patterns in the primary somatosensory cortex (S1). Exploiting these novel neuroscientific insights, we developed—and provide a proof of concept for—basic BCI communication by showing that binary (yes/no) answers encoded by somatosensory imagery can be decoded with high accuracy in simulated real-time (in 7 subjects) as well as in real-time (1 subject). This study demonstrates that body part-specific somatosensory imagery differentially activates somatosensory cortex in a topographically specific manner; evidence which was surprisingly still lacking in the literature. It also offers proof of concept for a novel somatosensory imagery-based fMRI-BCI control strategy, with particularly high potential for visually and motor-impaired patients. The strategy could also be transferred to lower MRI field strengths and to mobile functional near-infrared spectroscopy. Finally, given that communication BCIs provide the BCI user with a form of feedback based on their brain signals and can thus be considered as a specific form of neurofeedback, and that repeated use of a BCI has been shown to enhance underlying representations, we expect that the current BCI could also offer an interesting new approach for somatosensory rehabilitation training in the context of stroke and phantom limb pain.

Parent-child communication and preventive practices for child sexual abuse among the general population: A community-based study.

ObjectivesThis study was conducted to investigate parent–child communication and preventive practices centred on child sexual abuse among a cohort of Saudi population.MethodsA cross-sectional descriptive study was carried out at a single primary health care centre (PHC) in Dammam city, KSA. Parent–child communication and preventive practice were measured by a Self-Reporting Questionnaire with binary answers (yes/no). A total of 400 subjects were selected using a stratified random sampling method.ResultsThis study found that 82.5% (n = 329) of parents talked with their children about the latter's bodily privacy. With regard to supervision, most parents (91.7%) said that their children were under their direct supervision all the time. Other variables wear wearing appropriate clothes (94.7%), supervision (93.5%), and teaching a child about their bodily privacy (93.2%). A majority of the respondents (76.2%) were achieved a good preventive practice score, while 95 (23.8%) were observed to have poor communication practice. Logistic regression analyses for gender (p < 0.020; odds ratio 3.031; 95% CI), marital status (p < 0.026; odds ratio 0.081; 95% CI), and family size of more than one wife (p < 0.041; odds ratio 0.081; 95% CI) were significantly associated with good preventive practice.ConclusionThe study showed that parent–child communication practices in KSA seem to be reasonably good. Further parental supervision of children with respect to sexual abuse needs attention. Parents are advised to communicate with their children and to provide them with information and material on the prevention of sexual abuse.

The Routledge Handbook of English Language Teaching

Teacher trainers and conference presenters are often asked questions along the lines of ‘Does correction work?’, where the questioner seems to be hoping for one of two definite answers. But instead of saying ‘yes’ or ‘no’ we often find ourselves coming up with something like ‘Well, it depends…’. That’s because complex questions which demand binary answers (e.g. ‘Should the United Kingdom remain a member of the European Union or leave the European Union?’—the question put to the British electorate back in 2016) seldom produce workable and useful solutions. Look at all the trouble that referendum has caused—because things are invariably more complicated than a simple two-way choice! (And to bring things ‘closer to home’ for us language-teaching specialists it is worth pointing out that the influence on language teaching from Europe-wide thinking has been profound. The loss of any contributing member of those discussions—like the UK—would have quite an effect).

Predicting burn patient mortality with electronic medical records

BackgroundAlthough there exists robust literature on mortality-associated factors in burn patients, it is not known how electronic medical records affect outcomes. Using burn injury as a surgical care model of information and communication, we hypothesized that functionality and interoperability of the electronic medical record could serve as determinants of outcome. MethodsWe used the state inpatient databases for New York, Washington, California, and Florida for the years 2009 and 2010 for all states, with the additional years of 2012 and 2013 for New York (n = 6,002), and the respective data from the American Hospital Association Information Technology survey. Using International Classification of Diseases, Ninth Revision, codes, we included burn patients and characterized total body surface area burned. We summed the binary answers to questions 1 and 2 and question 3 from the American Hospital Association Information Technology survey to make continuous functionality and interoperability scores. Mortality was predicted using extreme gradient boosting in Python. ResultsIn each state in which our models had an accuracy and area under the curve of more than 0.90, electronic medical record functionality but not interoperability was a significant predictor in New York, California, and Florida. Important predictors in each state were, age, duration of stay, total body surface area burned/severity, and total charges. Electronic medical record functionality was more important than all comorbidities except for coagulopathies and electrolyte disorders. Higher functionality scores were associated with mortality (P < .01). ConclusionOur data support our hypothesis that electronic medical records may be associated with mortality in burn patients; however, electronic medical records are not having the intended impact on outcomes, and further research needs to elucidate exactly how electronic medical records are being used in clinical settings.