bluuebunny/crossref_metadata_2025_split huggingface dataset:
Emotional Support from AI Chatbots: Should a Supportive Partner Self-Disclose or Not?
Authors: Meng Jingbo , Dai Yue (Nancy) 
Published: April 2021

Abstract
This study examined how and when a chatbot’s emotional support was effective in reducing people’s stress and worry. It compared emotional support from chatbot versus human partners in terms of its process and conditional effects on stress/worry reduction. In an online experiment, participants discussed a personal stressor with a chatbot or a human partner who provided none, or either one or both of emotional support and reciprocal self-disclosure. The results showed that emotional support from a conversational partner was mediated through perceived supportiveness of the partner to reduce stress and worry among participants, and the link from emotional support to perceived supportiveness was stronger for a human than for a chatbot. A conversational partner’s reciprocal self-disclosure enhanced the positive effect of emotional support on worry reduction. However, when emotional support was absent, a solely self-disclosing chatbot reduced even less stress than a chatbot not providing any response to participants’ stress.

Lay Summary
In recent years, AI chatbots have increasingly been used to provide empathy and support to people who are experiencing stressful times. This study compared emotional support from a chatbot compared to that of a human who provided support. We were interested in examining which approach could best effectively reduce people’s worry and stress. When either a person or a chatbot was able to engage with a stressed individual and tell that individual about their own experiences, they were able to build rapport. We found that this type of reciprocal self-disclosure was effective in calming the worry of the individual. Interestingly, if a chatbot only reciprocally self-disclosed but offered no emotional support, the outcome was worse than if the chatbot did not respond to people at all. This work will help in the development of supportive chatbots by providing insights into when and what they should self-disclose.
Post-pancreatic necrosectomy biliary fistula: thinking beyond the box
Authors: Pandit Narendra , Awale Laligen , Deo Kunal Bikram , Pradhan Bickram 
Published: April 2021

ABSTRACT
Walled-off pancreatic necrosis is a challenging problem and pancreatic necrosectomy is associated with significant morbidity and mortality. Following necrosectomy, postoperative bile leak is a rare complication. We present such a case of delayed bile leak from the distal common bile duct in an 81-year-old lady following pancreatic necrosectomy, which was successfully managed by endoscopic stenting.
Hospital Presentation for Self-Harm in Youth as a Risk Marker for Later Psychotic and Bipolar Disorders: A Cohort Study of 59 476 Finns
Authors: Bolhuis Koen , Lång Ulla , Gyllenberg David , Kääriälä Antti , Veijola Juha , Gissler Mika , Kelleher Ian 
Published: April 2021

Abstract
Expanding clinical strategies to identify high risk groups for psychotic and bipolar disorders is a research priority. Considering that individuals diagnosed with psychotic and bipolar disorder are at high risk of self-harm, we hypothesised the reverse order relationship would also be true (ie, self-harm would predict psychotic/bipolar disorder). Specifically, we hypothesised that hospital presentation for self-harm would be a marker of high risk for subsequent development of psychotic/bipolar disorder and sought to test this hypothesis in a large population sample. This prospective register-based study included everyone born in Finland in 1987, followed until age 28 years (N = 59 476). We identified all hospital records of self-harm presentations, as well as all ICD-10 healthcare registrations of first diagnoses of psychotic and bipolar disorders. Cox proportional hazards models were used to assess the relationship between self-harm and psychotic/bipolar disorders. Of all individuals who presented to hospital with self-harm (n = 481), 12.8% went on to receive a diagnosis of psychosis (hazard ratio [HR] = 6.03, 95% confidence interval [CI] 4.56–7.98) and 9.4% a diagnosis of bipolar disorder (HR = 7.85, 95% CI 5.73–10.76) by age 28 years. Younger age of first self-harm presentation was associated with higher risk—for individuals who presented before age 18 years, 29.1% developed a psychotic or bipolar disorder by age 28 years. Young people who present to hospital with self-harm are at high risk of future psychotic and bipolar disorders. They represent an important cohort for the prevention of serious mental illness.
Weight change and resolution of fatty liver in normal weight individuals with nonalcoholic fatty liver disease
Authors: Sinn Dong Hyun , Kang Danbee , Cho Soo Jin , Paik Seung Woon , Guallar Eliseo , Cho Juhee , Gwak Geum-Youn 
Published: April 2021

Objectives
Obesity is a well-known risk factor for nonalcoholic fatty liver disease (NAFLD), and weight reduction is primarily recommended for managing the disease. However, some NAFLD patients have a normal weight (lean NAFLD), and whether weight reduction is also recommendable for lean NAFLD patients remains unclear.

Methods
We conducted a longitudinal study of 16 738 adults (average age, 50.5 years; lean NAFLD, 2383 participants) with NAFLD at baseline who underwent repeated health check-up examinations, including bodyweight measurements and abdominal ultrasonography from January 2003 through December 2013.

Results
During 68 389 person-years of follow-up (median follow-up of 3.00 years), 5819 patients had a fatty liver resolution. Compared with participants who had no weight reduction or increased weight, the fully adjusted hazard ratios for fatty liver resolution in participants with 0–4.9, 5–9.9 and 10% or more weight reduction were 1.67 [95% confidence interval (CI), 1.57–1.77], 3.36 (95% CI, 3.09–3.65) and 5.50 (95% CI, 4.83–6.27), respectively. The association between weight reduction and the fatty liver resolution was stronger in overweight/obese NAFLD participants but was also evident in lean NAFLD participants in a dose-dependent manner. In spline regression models, the association between weight change and the fatty liver resolution was linear among participants with normal weight.

Conclusion
There was a dose-dependent association between weight reduction and fatty liver resolution in both lean and overweight/obese NAFLD patients. This finding suggests weight reduction as a primary recommendation for lean NAFLD patients as in overweight/obese NAFLD patients.
Applying a machine learning modelling framework to predict delayed linkage to care in patients newly diagnosed with HIV in Mecklenburg County, North Carolina, USA
Authors: Chen Shi , Owolabi Yakubu , Dulin Michael , Robinson Patrick , Witt Brian , Samoff Erika 
Published: April 2021

Background:
Machine learning has the potential to help researchers better understand and close the gap in HIV care delivery in large metropolitan regions such as Mecklenburg County, North Carolina, USA.

Objectives:
We aim to identify important risk factors associated with delayed linkage to care for HIV patients with novel machine learning models and identify high-risk regions of the delay.

Methods:
Deidentified 2013–2017 Mecklenburg County surveillance data in eHARS format were requested. Both univariate analyses and machine learning random forest model (developed in R 3.5.0) were applied to quantify associations between delayed linkage to care (>30 days after diagnosis) and various risk factors for individual HIV patients. We also aggregated linkage to care by zip codes to identify high-risk communities within the county.

Results:
Types of HIV-diagnosing facility significantly influenced time to linkage; first diagnosis in hospital was associated with the shortest time to linkage. HIV patients with lower CD4+ cell counts (<200/ml) were twice as likely to link to care within 30 days than those with higher CD4+ cell count. Random forest model achieved high accuracy (>80% without CD4+ cell count data and >95% with CD4+ cell count data) to predict risk of delay in linkage to care. In addition, we also identified top high-risk zip codes of delayed linkage.

Conclusion:
The findings helped public health teams identify high-risk communities of delayed HIV care continuum across Mecklenburg County. The methodology framework can be applied to other regions with HIV epidemic and challenge of delayed linkage to care.
COVID transmission and contact tracing using WHO risk assement tool among frontline healthcare workers : Insights from a South Indian tertiary care centre
Authors: Moni Merlin , Kulirankal Kiran G , Prasanna Preetha , Mary Ann , Thomas Elizabeth Mary , Sundaram Rejitha P , Babu Binil , Bindu Veena , Edathadathil Fabia , Madathil Sai Bala , Beena K V , Sathyapalan Dipu T 
Published: April 2021

AbstractBackgroundThe high exposure risk to COVID among frontline heathcare workers was a major challenge to healthcare systems across the globe that warranted close monitoring through risk assessment and contact tracing strategies. The objective of our study was to characterize exposure risk factors for transmission and subsequent COVID positivity among the frontlinehealthcare workers in our institution during the pandemic period.MethodsThe retrospective observational study conducted over a period of 6 months from June 2020 to November 2020 at a 1300-bedded South Indian tertiary care centre included frontline healthcare workers who were assessed for their identified encounter with COVID positive individual using a modified WHO COVID risk assessment tool. Additional risk attributes of exposure characterized among COVID positive healthcare workers comprised of shared space, cluster related transmissions and multiple instances of exposure to COVID.ResultsAmong a total of 4744 contacts with COVID positive individuals assessed for risk stratification during the study period, 942 (19.8%) were high risk and 3802 (80.2%) were low risk exposures respectively. 106 (2.2%) turned COVID positive during the surveillance period of 14 days. Frontline workers working in COVID areas had significant low COVID rates as compared to other areas (N=1, 0.9%). The average monthly COVID positivity rates being 1.66%, the attack rates among high risk and low risk contacts among the total HCWs screened were 5% (46/942) and 1.57% (60/3802) respectively. Shared space (70%) and IPC breaches (66%) were found to be highly prevalent in the COVID positive cohort, along with maskless encounters (43%) and multiple exposure (39%). The attack rate among the 6 identified COVID cluster groups (5.5%) were found to be higher than the attack rate (2.2%) noted among the total contacts screened and no significant association was observed between risk categories in the clusters.DiscussionOur study highlights higher risk of COVID positivity among high risk contacts as compared to low risk contacts. However, the high COVID positivity rate in low risk group among cluster transmissions and its lack of association with risk assessment highlight the suboptimal utility of the risk assessment strategy among cluster groups.
Violence and Non-fatal Injuries among Vietnamese In-school Adolescents: National Prevalence Estimates and Associated Factors
Authors: Le Phuong Anh , Hoang Van Minh , Tran Thi Tuyet Hanh , Khuong Quynh Long , Takeuchi Momoe , Nguyen Tuan Lam , Pham Thi Quynh Nga , Le Van Tuan , Tran Quoc Bao , Park Kidong 
Published: April 2021

AbstractBackgroundSchool violence and injury are major public health problems worldwide; however, current information on these issues in Vietnam is lacking. We aim to investigate the prevalence of violence and non-fatal injury and associated factors among Vietnamese adolescents aged 13-17 years old.MethodsWe used data from the 2019 Vietnam Global School-based Student Health Survey (GSHS), which is a nationally representative survey developed by the World Health Organization to monitor behavioral risk factors among school-aged students. The Vietnam GSHS 2019 was conducted in 20 provinces and cities, with a sample size of 7,690 male and female students aged 13-17 years old.ResultsWe found the prevalence of violence and non-fatal injury was 14.5% and 21.4%, respectively. Common risk factors for both violence and non-fatal injuries included cigarette smoking, alcohol use, mental health problems, and living with neither parent; violence was also a risk factor for non-fatal injuries. Student older age was associated with lower odds of school violence. Parents played an important role in preventing violence among female students and non-fatal injuries in both genders.ConclusionsFuture policies should consider individual factors as well as adolescent-parent bonding, to mitigate the burden of violence and injury among in-school adolescents in Vietnam.
The impact of technology systems and professional support in digital mental health interventions: a secondary meta-analysis
Authors: Sasseville Maxime , LeBlanc Annie , Tchuente Jack , Boucher Mylène , Dugas Michèle , Gisèle Mbemba , Barony Romina , Chouinard Maud-Christine , Beaulieu Marianne , Beaudet Nicolas , Skidmore Becky , Cholette Pascale , Aspiros Christine , Larouche Alain , Chabot Guylaine , Gagnon Marie-Pierre 
Published: April 2021

ABSTRACTBackgroundA rapid review of systematic reviews was conducted to assess the effectiveness of digital mental health interventions for people with a chronic disease. Although it provided an overview of the evidence, it offered limited understanding of ethe types of interventions that were the most effective. The aim of this study was to perform a meta-analysis of primary studies identified in this rapid review of systematic reviews by focusing on the needs of knowledge users.MethodsThis secondary meta-analysis follows a rapid review of systematic reviews, a virtual workshop with knowledge users to identify research questions and a modified Delphi study to guide research methods. We conducted a secondary analysis of the primary studies identified in the rapid review. Two reviewers independently screened the titles and abstracts and applied inclusion criteria: RCT design using a digital mental health intervention in a population of adults with another chronic condition, published after 2010 in French or English, and including an outcome measurement of anxiety or depression.Results708 primary studies were extracted from the systematic reviews and 84 primary studies met the inclusion criteria Digital mental health interventions were significantly more effective than in-person care for both anxiety and depression outcomes. Online messaging was the most effective technology to improve anxiety and depression scores; however, all technology types were effective. Interventions partially supported by healthcare professionals were more effective than self-administered.ConclusionsWhile our meta-analysis identifies digital intervention’s characteristics that are more effective, all technologies and levels of support can be used considering implementation context and population.Review registrationThe protocol for this review is registered in the National Collaborating Centre for Methods and Tools (NCCMT) COVID-19 Rapid Evidence Service (ID 75).
Molecular analysis suggests oligoclonality and metastasis of endometriosis lesions across anatomically defined subtypes
Authors: Praetorius Teresa H. , Leonova Anna , Lac Vivian , Senz Janine , Tessier-Cloutier Basile , Nazeran Tayyebeh M. , Köbel Martin , Grube Marcel , Kraemer Bernhard , Yong Paul J. , Kommoss Stefan , Anglesio Michael S. 
Published: April 2021

AbstractEndometriosis symptoms are heterogeneous with controversy on whether it constitutes a single disease or multiple distinct types. Our previous work found recurrent somatic cancer-driver alterations in endometriosis; however, these have not been found ubiquitously. A handful of cases spread across studies also suggest mutations might be shared (clonal) between lesions of the same type. As current classification systems correlate poorly with symptoms or outcomes, somatic genomics may improve the current system. Here, we investigate heterogeneity of somatic cancer-driver mutations within patients and across endometriosis types. We examined anatomically distinct types of endometriosis (ovarian, deep infiltrating, and superficial endometriosis) in 27 individual patients all of whom had at least two types of endometriosis. Specimens were analyzed using high-sensitivity targeted sequencing with orthogonal validation from droplet digital PCR and mutation-surrogate immunohistochemistry. Results found 13/27 patients had informative somatic driver mutation in endometriosis, 9/13 had identical mutations across distinct lesions. Endometriomas tended to have a higher mutational complexity, with functionally redundant driver mutations in same gene and within the same lesions.Our data are consistent with clonality across endometriosis lesions regardless of subtype. Further the finding of redundancy in mutations with the same gene and lesions is also consistent with endometriosis representing an oligoclonal disease with dissemination likely to consist of multiple epithelial clones travelling together. This suggests the current anatomically defined classification of endometriosis does not fully recognize the etiology of the disease. A novel classification should take into account genomic and other molecular features. These findings could further contribute to development of a more personalized endometriosis diagnosis and care.
The impact of population mobility on COVID-19 incidence and socioeconomic disparities at the sub-city level in 314 Latin American cities
Authors: Kephart Josiah L. , Delclòs-Alió Xavier , Bilal Usama , Sarmiento Olga L. , Barrientos-Gutiérrez Tonatiuh , Ramirez-Zea Manuel , Quistberg D. Alex , Rodríguez Daniel A. , Roux Ana V. Diez 
Published: April 2021

AbstractBackgroundLittle is known about the impact of changes in mobility at the sub-city level on subsequent COVID-19 incidence or the contribution of mobility to socioeconomic disparities in COVID-19 incidence.MethodsWe compiled aggregated mobile phone location data, COVID-19 confirmed cases, and features of the urban and social environments to analyze linkages between population mobility, COVID-19 incidence, and educational attainment at the sub-city level among cities with >100,000 inhabitants in Argentina, Brazil, Colombia, Guatemala, and Mexico from March to August 2020. We used mixed effects negative binomial regression to examine longitudinal associations between changes in weekly mobility (lags 1-6 weeks) and subsequent COVID-19 incidence at the sub-city level, adjusting for urban environmental factors.FindingsAmong 1,031 sub-cities representing 314 cities in five Latin American countries, 10% higher weekly mobility was associated with 8.5% (95% CI 7.4% to 9.5%) higher weekly COVID-19 incidence the following week. This association gradually declined as the lag between mobility and COVID-19 incidence increased and was not different from the null at a six-week lag. We found evidence that suggests differences in mobility reductions are a driver of socioeconomic disparities in COVID-19 incidence.InterpretationLower population movement within a sub-city is associated with lower risk of subsequent COVID-19 incidence among residents of that sub-city. Implementing policies that reduce population mobility at the sub-city level may be an impactful COVID-19 mitigation strategy that takes equity into consideration and reduces economic and social disruption at the city or regional level.FundingWellcome Trust
Adverse effects of COVID-19 vaccination: machine learning and statistical approach to identify and classify incidences of morbidity and post-vaccination reactogenicity
Authors: Ahamad Md. Martuza , Aktar Sakifa , Uddin Md. Jamal , Rashed-Al-Mahfuz Md. , Azad AKM , Uddin Shahadat , Alyami Salem A. , Sarker Iqbal H. , Liò Pietro , Quinn Julian M.W. , Moni Mohammad Ali 
Published: April 2021

ABSTRACTGood vaccine safety and reliability are essential to prevent infectious disease spread. A small but significant number of apparent adverse reactions to the new COVID-19 vaccines have been reported. Here, we aim to identify possible common causes for such adverse reactions with a view to enabling strategies that reduce patient risk by using patient data to classify and characterise patients those at risk of such reactions. We examined patient medical histories and data documenting post-vaccination effects and outcomes. The data analyses were conducted by different statistical approaches followed by a set of machine learning classification algorithms. In most cases, similar features were significantly associated with poor patient reactions. These included patient prior illnesses, admission to hospitals and SARS-CoV-2 reinfection. The analyses indicated that patient age, gender, allergic history, taking other medications, type-2 diabetes, hypertension and heart disease are the most significant pre-existing factors associated with risk of poor outcome and long duration of hospital treatments, pyrexia, headache, dyspnoea, chills, fatigue, various kind of pain and dizziness are the most significant clinical predictors. The machine learning classifiers using medical history were also able to predict patients most likely to have complication-free vaccination with an accuracy score above 85%. Our study identifies profiles of individuals that may need extra monitoring and care (e.g., vaccination at a location with access to comprehensive clinical support) to reduce negative outcomes through classification approaches. Important classifiers achieving these reactions notably included allergic susceptibility and incidence of heart disease or type-2 diabetes.
A machine learning based aging measure among middle-aged and older Chinese adults: the China Health and Retirement Longitudinal Study
Authors: Cao Xinqi , Yang Guanglai , Jin Xurui , He Liu , Li Xueqin , Zheng Zhoutao , Liu Zuyun , Wu Chenkai 
Published: April 2021

AbstractBackgroundBiological age (BA) has been accepted as a more accurate proxy of aging than chronological age (CA). This study aimed to use machine learning (ML) algorithms to estimate BA in the Chinese population.MethodsWe used data from 9,771 middle-aged and older (≥ 45 years) Chinese adults in the China Health and Retirement Longitudinal Study. We used several ML algorithms (e.g., Gradient Boosting Regressor, Random Forest, CatBoost Regressor, and Support Vector Machine) to develop new measures of biological aging (ML-BAs) based on physiological biomarkers. R-squared value and mean absolute error (MAE) were used to determine the optimal performance of these ML-BAs. We used logistic regression models to examine the associations of the best ML-BA and a conventional aging measure – Klemera and Doubal method-biological age (KDM-BA) we previously developed – with physical disability and mortality, respectively.ResultsThe Gradient Boosting Regression model performed best, resulting in a ML-BA with R-squared value of 0.270 and MAE of 6.519. This ML-BA was significantly associated with disability in basic activities of daily living, instrumental activities of daily living, lower extremity mobility, and upper extremity mobility, and mortality, with odds ratios ranging from 1% to 7% (per one-year increment in ML-BA, all P <0.001), independent of CA. These associations were generally comparable to that of KDM-BA.ConclusionThis study provides a valid ML-based measure of biological aging for middle-aged and older Chinese adults. These findings support the application of ML in geroscience research and help facilitate the understanding of the aging process.