bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine learning prediction of motor response after deep brain stimulation in Parkinson’s disease
Authors: Habets J , Duits A , Sijben L , De Greef B , Mulders A , Temel Y , Kuijf M , Kubben P , Herff C , Janssen M 
Published: October 2019

AbstractIntroductionDespite careful patient selection for subthalamic nucleus deep brain stimulation (STN DBS), some Parkinson’s disease patients show limited improvement of motor disability. Non-conclusive results from previous prediction studies maintain the need for a simple tool for neurologists that reliably predicts postoperative motor response for individual patients. Establishing such a prediction tool facilitates the clinician to improve patient counselling, expectation management, and postoperative patient satisfaction. Predictive machine learning models can be used to generate individual outcome predictions instead of correlating pre- and postoperative variables on a group level.MethodsWe developed a machine learning logistic regression prediction model which generates probabilities for experiencing weak motor response one year after surgery. The model analyses preoperative variables and is trained on 90 patients using a ten-fold cross-validation. We intentionally chose to leave out pre-, intra- and postoperative imaging and neurophysiology data, to ensure the usability in clinical practice.Weak responders (n = 27) were defined as patients who fail to show clinically relevant improvement on Unified Parkinson Disease Rating Scale (UPDRS) II, III or IV.ResultsThe model predicts weak responders with an average area under the curve of the receiver operating characteristic of 0.88 (standard deviation: 0.14), a true positive rate of 0.85 and a false positive rate of 0.25, and a diagnostic accuracy of 78%. The reported influences of the individual preoperative variables are useful for clinical interpretation of the model, but cannot been interpreted separately regardless of the other variables in the model.ConclusionThe very good diagnostic accuracy of the presented prediction model confirms the utility of machine-learning based motor response prediction one year after STN DBS implantation, based on clinical preoperative variables.After reproduction and validation in a prospective cohort, this prediction model holds a tremendous potential to be a supportive tool for clinicians during the preoperative counseling.
Examining the effect of smoking on suicidal ideation and attempts: A triangulation of epidemiological approaches
Authors: Harrison Ruth , Munafò Marcus R , Smith George Davey , Wootton Robyn E 
Published: October 2019

AbstractBackgroundPrevious literature has demonstrated a strong association between cigarette smoking and suicide-related behaviours, characterised as ideation, plans, attempts and suicide related death. This association has not previously been examined in a causal inference framework and has important implications for suicide prevention strategies.AimsWe aimed to examine the evidence for an association between smoking behaviours (initiation, smoking status, heaviness, lifetime smoking) and suicidal thoughts or attempts by triangulating across observational and Mendelian randomisation (MR) analyses.MethodsFirst, in the UK Biobank, we calculate observed associations between smoking behaviours and suicidal thoughts or attempts. Second, we used Mendelian randomisation (MR) to explore the relationship between smoking and suicide using genetic variants as instruments to reduce bias from residual confounding and reverse causation.ResultsOur observational analysis showed a relationship between smoking behaviour and suicidal behaviour, particularly between smoking initiation and suicidal attempts (OR = 2.07, 95% CI = 1.91 to 2.26, p<0.001). The MR analysis and single SNP analysis, however, did not support this. Despite past literature showing a positive dose-response relationship our results showed no clear evidence for a causal effect of smoking on suicidal behaviours.ConclusionThis was the first MR study to explore the effect of smoking on suicidal behaviours. Our results suggest that, despite observed associations, there is no strong evidence for a causal effect of smoking behaviour on suicidal behaviour. Our evidence suggests that further research is needed into alternative risk factors for suicide which might make better intervention targets.
The day-to-day experiences of caring for children with Osteogenesis Imperfecta: A qualitative descriptive study
Authors: Castro Aimee R. , Marinello Jessica , Chougui Khadidja , Morand Marilyn , Bilodeau Claudette , Rauch Frank , Tsimicalis Argerie 
Published: October 2019

AbstractAims and objectivesThis study aimed to explore the day-to-day experiences of caregivers who are caring for children with Osteogenesis Imperfecta (OI).BackgroundOI is a rare genetic condition known to cause bone fragility. Family caregivers, such as parents, of children with OI play an important role in helping these children live well at home.DesignThe design was qualitative description.MethodsA qualitative descriptive study was conducted which adheres to the COREQ guidelines. Adult caregivers (n=18) of children with OI were recruited at a children’s hospital in Montréal, Canada to participate in individual interviews. The interviews were transcribed verbatim and inductively thematically analysed.ResultsThe following caregiving themes were identified in these interviews: regular day-to-day caregiving activities, including morning routines, evening routines, and the facilitation of their child’s mobilization; periods that made the caregiving routine more challenging, such as fractures, surgeries, and pain; and the long-term strategies caregivers developed to support day-to-day care, such as managing the environment, accessing medical and school resources, and coordinating care and respite.ConclusionsThe results showcase what being a caregiver for a child with OI involves on a day-to-day basis.Relevance to clinical practiceThe recommendations include suggestions for future clinical, policy, and research endeavours to develop better policies and interventions to support the unique needs of family caregivers of children with OI. These recommendations may be relevant to other clinicians and policymakers working with families living with rare and chronic physical conditions.
Measuring Total Healthcare Utilization Among Intimate Partner Violence Survivors In Primary Care
Authors: Logeais Mary E. , Wang Qi , Renner Lynette M. , Clark Cari Jo 
Published: October 2019

ABSTRACTRising health care costs are influenced by health care utilization, which encompasses hospital, ambulatory and non-face-to-face episodes of care. In this study, we created a novel a health care utilization-scoring tool that was used to examine whether one psychosocial factor, intimate partner violence (IPV), leads to higher utilization of health care services when controlling for relevant confounders. We sought to fill gaps about how social and behavioral issues impact utilization—particularly non-face-to-face episodes of care.We conducted a retrospective cross-sectional study in 2017 examining patients seen at 11 University-affiliated primary care clinics from January 2015 to December 2016 who were screened for IPV. A total of 31,305 patients were screened, of which 280 screened positive. We controlled for medical complexity by deriving the revised Charlson Comorbidity Index for each patient. We calculated a novel utilization score, which was a weighted sum of hospital, ambulatory and non-face-to-face encounters. Missed appointments were also measured.IPV-positive and IPV-negative patients were similar with respect medical complexity. IPV-positive patients had significantly higher mean utilization scores (54 vs. 40, p<0.001) and more missed appointments (3 vs. 1.3, p<0.001). IPV was associated with increased total utilization (p=0.015), as well as non-face-to-face and ambulatory visits (p=0.025 and p=0.015, respectively) for female patients and was associated with more missed appointments for both males and females (p< .001).These data support more inclusive population-specific interventions focusing on social determinants of health to reduce both face-to-face and non-face-to-face utilization, which may improve health care expenditures, outcomes and provider satisfaction.
Estimating youth diabetes risk using NHANES data and machine learning
Authors: Vangeepuram Nita , Liu Bian , Chiu Po-hsiang , Wang Linhua , Pandey Gaurav 
Published: October 2019

AbstractBackgroundPrediabetes and diabetes mellitus (preDM/DM) have become alarmingly prevalent among youth in recent years. However, simple questionnaire-based screening tools to reliably assess diabetes risk are only available for adults, not youth.MethodsAs a first step in developing such a tool, we used a large-scale dataset from the National Health and Nutritional Examination Survey (NHANES) to examine the performance of a published pediatric clinical screening guideline in identifying youth with preDM/DM based on American Diabetes Association diagnostic biomarkers. We assessed the agreement between the clinical guideline and biomarker criteria using established evaluation measures (sensitivity, specificity, positive/negative predictive value, F-measure for the positive/negative preDM/DM classes, and Kappa). We also compared the performance of the guideline to those of machine learning (ML) based preDM/DM classifiers derived from the NHANES dataset.ResultsApproximately 29% of the 2858 youth in our study population had preDM/DM based on biomarker criteria. The clinical guideline had a sensitivity of 43.1% and specificity of 67.6%, positive/negative predictive values of 35.2%/74.5%, positive/negative F-measures of 38.8%/70.9%, and Kappa of 0.1 (95%CI: 0.06-0.14). The performance of the guideline varied across demographic subgroups. Some ML-based classifiers performed comparably to or better than the screening guideline, especially in identifying preDM/DM youth (p=5.23×10−5).ConclusionsWe demonstrated that a recommended pediatric clinical screening guideline did not perform well in identifying preDM/DM status among youth. Additional work is needed to develop a simple yet accurate screener for youth diabetes risk, potentially by using advanced ML methods and a wider range of clinical and behavioral health data.Key MessagesAs a first step in developing a youth diabetes risk screening tool, we used a large-scale dataset from the National Health and Nutritional Examination Survey (NHANES) to examine the performance of a published pediatric clinical screening guideline in identifying youth with prediabetes/diabetes based on American Diabetes Association diagnostic biomarkers.In this cross-sectional study of youth, we found that the screening guideline correctly identified 43.1% of youth with prediabetes/diabetes, the performance of the guideline varied across demographic subgroups, and machine learning based classifiers performed comparably to or better than the screening guideline in identifying youth with prediabetes/diabetes.Additional work is needed to develop a simple yet accurate screener for youth diabetes risk, potentially by using advanced ML methods and a wider range of clinical and behavioral health data.
Mathematical modeling of directed acyclic graphs to explore competing causal mechanisms underlying epidemiological study data
Authors: Havumaki Joshua , Eisenberg Marisa C. 
Published: October 2019

1AbstractAccurately estimating the effect of an exposure on an outcome requires understanding how variables relevant to a study question are causally related to each other. Directed acyclic graphs (DAGs) are used in epidemiology to understand causal processes and determine appropriate statistical approaches to obtain unbiased measures of effect. Compartmental models (CMs) are also used to represent different causal mechanisms, by depicting flows between disease states on the population level. In this paper, we extend a mapping between DAGs and CMs to show how DAG–derived CMs can be used to compare competing causal mechanisms by simulating epidemiological studies and conducting statistical analyses on the simulated data. Through this framework, we can evaluate how robust simulated epidemiological study results are to different biases in study design and underlying causal mechanisms. As a case study, we simulated a longitudinal cohort study to examine the obesity paradox: the apparent protective effect of obesity on mortality among diabetic ever-smokers, but not among diabetic never-smokers. Our simulations illustrate how study design bias (e.g., reverse causation), can lead to the obesity paradox. Ultimately, we show the utility of transforming DAGs into in silico laboratories within which researchers can systematically evaluate bias, and inform analyses and study design.
PROBLEM BASED LEARNING APPLIED TO PRACTICE IN MEDICAL SCHOOLS IN BRAZIL: A MINI-SYSTEMATIC REVIEW
Authors: Bussolaro Frederico Alberto , Thereza-Bussolaro Claudine 
Published: October 2019

ABSTRACTBackgroundActive learning is a well-established educational methodology in medical schools worldwide, although its implementation in Brazilian clinical settings is quite challenging. The objective of this study is to review the literature in a systematic manner to find and conduct a reflective analysis of how problem-based learning (PBL) has been applied to clinical teaching in medical schools in Brazil.Material & methodsA systematic literature search was conducted in three databases. A total of 250 papers related to PBL in Brazilian medical schools were identified through the database searches. Four studies were finally selected for the review.ResultsFour fields of medicine were explored on the four selected papers: gynecology/family medicine, medical semiology, psychiatry, and pediatrics. Overall, all the papers reported some level of strategic adaptability of the original PBL methodology to be applied in the Brazilian medical school’s curricula and to the peculiar characteristics specific to Brazil.ConclusionPBL application in Brazilian medical schools require some level of alteration from the original format, to better adapt to the characteristics of Brazilian students’ maturity, health system priorities and the medical labor market.
Safety of Flexible Sigmoidoscopy in Pregnant Patients with known or suspected Inflammatory Bowel Disease
Authors: Ko Myung S. , Rudrapatna Vivek A. , Avila Patrick , Mahadevan Uma 
Published: October 2019

AbstractI.Background and AimsLower gastrointestinal endoscopy is the gold standard for the diagnosis and staging of Inflammatory Bowel Disease (IBD). However, there is limited safety data in pregnant populations, resulting in conservative society guidelines and practice patterns favoring diagnostic delay. The aim of this study is to investigate if the performance of flexible sigmoidoscopy is associated with adverse events in pregnant patients with known or suspected IBD.II.MethodsA retrospective cohort study was conducted at the University of California San Francisco (UCSF) between April 2008 and April 2019. Female patients aged between 18 and 48 years who were pregnant at the time of endoscopy were identified. All patient records were reviewed to determine disease, pregnancy course, and lifestyle factors. Two independent reviewers performed the data abstraction. Adverse events were assessed for temporal relation (defined as within 4 weeks) with endoscopy. Any discrepancies in the two reviewers’ data were reviewed by a third independent investigator. Descriptive statistics of data were calculated, and comparison of continuous and categorical data were made using a one-sided Wilcoxon rank-sum test and Fisher’s exact test, respectively.III.ResultsWe report the outcomes of 48 pregnant patients across all trimesters who underwent lower endoscopy for suspected or established IBD. There were no hospitalizations or adverse obstetric events temporally associated with sigmoidoscopy. 78% of patients experienced a change in treatment following sigmoidoscopy. 12% of the patients with known IBD were found to have no endoscopic evidence of disease activity despite symptoms.IV.ConclusionsLower endoscopy in the pregnant patient with known or suspected IBD is low risk and affects therapeutic decision making. It should not be delayed in patients with appropriate indications.
Can the impact of childhood adiposity on disease risk be reversed? A Mendelian randomization study
Authors: Richardson Tom G. , Sanderson Eleanor , Elsworth Benjamin , Tilling Kate , Davey Smith George 
Published: October 2019

AbstractObjectiveTo evaluate whether early life adiposity has an independent effect on later life disease risk or whether its influence is mediated by adulthood body mass index (BMI).DesignTwo-sample univariable and multivariable Mendelian randomization.SettingThe UK Biobank (UKB) prospective cohort study and four large-scale genome-wide association study (GWAS) consortia.Participants453,169 participants enrolled in the UKB and a combined total of over 700,000 individuals from different GWAS consortia.ExposuresMeasured BMI during adulthood (mean age: 56.5) and self-reported adiposity at age 10.Main outcome measuresCoronary artery disease (CAD), type 2 diabetes (T2D), breast cancer and prostate cancer.ResultsIndividuals with genetically predicted higher BMI in early life had increased odds of CAD (OR:1.49, 95% CI:1.33-1.68) and T2D (OR:2.32, 95% CI:1.76-3.05) based on univariable MR (UVMR) analyses. However, there was little evidence of a direct effect (i.e. not via adult BMI) based on multivariable MR (MVMR) estimates (CAD OR:1.02, 95% CI:0.86-1.22, T2D OR:1.16, 95% CI:0.74-1.82). In the MVMR analysis of breast cancer risk, there was strong evidence of a protective direct effect for early BMI (OR:0.59, 95% CI:0.50-0.71), although adult BMI did not appear to have a direct effect on this outcome (OR:1.08, 95% CI:0.93-1.27). Adding age of menarche as an additional exposure provided weak evidence of a total causal effect (UVMR OR:0.98, 95% CI:0.91-1.06) but strong evidence of a direct causal effect, independent of early and adult BMI (MVMR OR:0.90, 95% CI:0.85-0.95). Weak evidence of a causal effect was observed in the MVMR analysis of prostate cancer (early life BMI OR:1.06, 95% CI:0.81-1.40, adult BMI OR:0.87, 95% CI:0.70-1.08).ConclusionsOur findings suggest that increased CAD and T2D risk attributed to early life adiposity can be mitigated if individuals reduce their weight in later life. However, having a low BMI during childhood may increase risk of breast cancer regardless of changes to weight in later life, with timing of puberty also putatively playing an important role.
Potential impact of outpatient stewardship interventions on antibiotic exposures of bacterial pathogens
Authors: Tedijanto Christine , Grad Yonatan H , Lipsitch Marc 
Published: October 2019

AbstractThe relationship between antibiotic stewardship and population levels of antibiotic resistance remains unclear. In order to better understand shifts in selective pressure due to stewardship, we use publicly available data to estimate the effect of changes in prescribing on exposures to frequently used antibiotics experienced by potentially pathogenic bacteria that are asymptomatically colonizing the microbiome. We quantify this impact under four hypothetical stewardship strategies. In one scenario, we estimate that elimination of all unnecessary outpatient antibiotic use could avert 6 to 48% (IQR: 17 to 31%) of exposures across pairwise combinations of sixteen common antibiotics and nine bacterial pathogens. All scenarios demonstrate that stewardship interventions, facilitated by changes in clinician behavior and improved diagnostics, have the opportunity to broadly reduce antibiotic exposures across a range of potential pathogens. Concurrent approaches, such as vaccines aiming to reduce infection incidence, are needed to further decrease exposures occurring in “necessary” contexts.
Quantifying the success of measles vaccination campaigns in the Rohingya refugee camps
Authors: Chin Taylor , Buckee Caroline O. , Mahmud Ayesha S. 
Published: October 2019

AbstractIn the wake of the Rohingya population’s mass migration from Myanmar, one of the world’s largest refugee settlements was constructed in Cox’s Bazar, Bangladesh to accommodate nearly 900,000 new refugees. Refugee populations are particularly vulnerable to infectious disease outbreaks due to many population and environmental factors. A large measles outbreak, with over 2,500 cases, occurred among the Rohingya population between September and December 2017. Here, we estimate key epidemiological parameters and use a dynamic mathematical model of measles transmission to evaluate the effectiveness of the reactive vaccination campaigns in the refugee camps. We also estimate the potential for subsequent outbreaks under different vaccination coverage scenarios. Our modeling results highlight the success of the vaccination campaigns in rapidly curbing transmission and emphasize the public health importance of maintaining high levels of vaccination in this population, where high birth rates and historically low vaccination coverage rates create suitable conditions for future measles outbreaks.
Alpha-ketoglutarate, an endogenous metabolite, extends lifespan and compresses morbidity in aging mice
Authors: Shahmirzadi Azar Asadi , Edgar Daniel , Liao Chen-Yu , Hsu Yueh-Mei , Lucanic Mark , Shahmirzadi Arash Asadi , Wiley Christopher , Riley Rebeccah , Kaplowitz Brian , Gan Garbo , Kuehnemann Chisaka , Bhaumik Dipa , Campisi Judith , Kennedy Brian K , Lithgow Gordon J. 
Published: October 2019

AbstractThe decline in early life mortality since the 1950s has resulted in dramatic demographic shift towards aged population. Aging manifests as a decline in health, multiple organ dysfunction and increased vulnerability to diseases, which degrades quality of life. A verity of genetic and pharmacological interventions, mostly from non-vertebrate models, have been identified that can enhance lifespan. Whether these interventions extend healthspan, the disease free and functional period of life, has only sometimes been tested and is often a matter of debate. Human aging indices have been developed to assess elements of functional decline with aging (e.g. sarcopenia, cognitive function). However, corresponding comprehensive indices in mice are seldom applied to aging studies. To probe the relationship between healthspan and lifespan extension in mammals, we performed a series of longitudinal, clinically-relevant healthspan measurements. Metabolism and aging are tightly connected and specific perturbations of nutrient-sensing pathways can enhance longevity in laboratory animals. Here we show that alpha-ketoglutarate (delivered in the form of a Calcium salt, CaAKG), a key metabolite in tricarboxylic (TCA) cycle that is reported to extend lifespan in worms, can significantly extend lifespan and healthspan in mice. AKG is involved in various fundamental processes including collagen synthesis and epigenetic changes. Due to its broad roles in multiple biological processes, AKG has been a subject of interest for researchers in various fields. AKG also influences several age-related processes, including stem cell proliferation and osteoporosis. To determine its role in mammalian aging, we administered CaAKG in 18 months old mice and determined its effect on the onset of frailty and survival, discovering that the metabolite promotes longer, healthier life associated with a decrease in levels of inflammatory factors. Interestingly the reduction in frailty was more dramatic than the increase in lifespan, leading us to propose that CaAKG compresses morbidity.