bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Effects of Reflective Pauses on Performance in Simulation Training
Authors: Lee Joy Y. , Donkers Jeroen , Jarodzka Halszka , Sellenraad Géraldine , Faber Tjitske J.E. , van Merriënboer Jeroen J.G. 
Published: April 2023

Introduction
The reflective pause, taking a pause during performance to reflect, is an important practice in simulation-based learning. However, for novice learners, it is a highly complex self-regulatory skill that cannot stand alone without guidance. Using educational theories, we propose how to design cognitive and metacognitive aids to guide learners with the reflective pause and investigate its effects on performance in a simulation training environment.

Methods
These effects are examined in four aspects of performance: cognitive load, primary performance, secondary performance, and encapsulation. Medical students (N = 72) performed tasks in simulation training for emergency medicine, under 2 conditions: reflection condition (n = 36) where reflection was prompted and guided, and control condition (n = 36) without such reflection.

Results
The effects of reflective pauses emerged for 2 aspects of performance: cognitive load decreased and secondary performance improved. However, primary performance and encapsulation did not show significant difference.

Conclusions
The results demonstrate that reflective pauses with cognitive and metacognitive aids implemented can enhance some aspects of performance. We suggest that to secure these effects, feedback during reflection and an adaptation period should be provided.
Systematic mediation and interaction analyses of kidney function genetic loci in a general population study
Authors: Ghasemi-Semeskandeh Dariush , Emmert David , König Eva , Foco Luisa , Gögele Martin , Barin Laura , Fujii Ryosuke , Fuchsberger Christian , Peters Dorien J.M. , Pramstaller Peter P. , Pattaro Cristian 
Published: April 2023

AbstractChronic kidney disease (CKD) is a complex disease affecting >10% of the global population, with large between-and within-continent variability reflecting major environmental effects. To identify molecular targets for treatment, genome-wide association study meta-analyses (GWAMAs) of CKD-defining traits have identified hundreds of genetic loci in aggregated populations. However, while GWAMAs estimate the average allelic effect across studies, single population studies may be relevant to unravel specific mechanisms. To assess whether an individual study from a specific population could extend existing knowledge on kidney function genetics, we selected 147 kidney function relevant loci identified by a large GWAMA, assessing their association with the glomerular filtration rate estimated from serum creatinine (eGFRcrea) in the Cooperative Health Research In South Tyrol (CHRIS) study (n=10,146), conducted in an Alpine region where thyroid dysfunction is common. We replicated associations with single nucleotide polymorphisms (SNPs) at 11 loci, showing up-to-5.4 times larger effect sizes than in the corresponding GWAMA, not explainable by minor allele frequency differences. Systematic mediation analysis across 70 quantitative traits identified serum magnesium and the activated partial thromboplastin time as partial mediators of the eGFRcrea associations atSHROOM3andSLC34A1, respectively. Given that free triiodothyronine and thyroxine were effect modifiers across all loci, we conducted SNP-by-thyroid stimulating hormone (TSH) interaction analyses, identifying significant interactions atSTC1: SNPs had larger effects on eGFRcrea at higher TSH levels, possibly reflecting stanniocalcin-1 autocrine and paracrine role. Individual population studies can help characterize genetic associations. The interplay between phenotypes atSHROOM3andSLC34A1and the role of thyroid function as a genetic effect modifier warrant further investigations.
An accuracy study of the YouDiagnose disease predictive model based on a double-validated real-world dataset from the UK
Authors: Misro Aswini , Sharma Vikash , Kadoglou Naim 
Published: April 2023

AbstractThrough the utilisation of algorithms and data-driven analytics, predictive technology can be leveraged to provide clinicians with invaluable insight into their patients’ conditions, allowing for more accurate, informed, and timely decision-making in the fast-paced clinical environment. This study aims to provide a preliminary proof of concept with a double-validated real-world dataset from the UK. The YouDiagnose predictive model, developed using retrospective data from over 41,257 patients’ data, was assessed by testing it with a double-validated real-world dataset from the UK and the machine predictions have been compared here with the final diagnosis of the diseases as the gold standard. Out of the total of 433 cases, 60 cases had a mismatch in their prediction, all of them being cancer overdiagnosis, resulting in a lower specificity rate of 84.3%. The combined prediction accuracy at the first prediction level was 86% (n=373) while prediction 1-3 combined was successful in predicting diseases in 93% of the cases when evaluated against the gold standard. The model accurately predicted all 52 cases of cancer, indicating a 100% sensitivity rate. The study shows that the tool can be used in the frontline to accurately screen patients with a high level of confidence in the inclusion of cancer patients. This tool’s high sensitivity means that there is little chance of missing any cancer cases.
iSPAN: Improved prediction of outcomes post thrombectomy with Machine Learning
Authors: Kelly Brendan S , Mathur Prateek , Duignan John , Power Sarah , H Lee Edward , Huang Yuhao , Vaca Silvia D , Prolo Laura M , Yeom Kristen W , Lawlor Aonghus , Killeen Ronan P 
Published: April 2023

AbstractBackgroundThis study aimed to develop and evaluate a machine learning model and a novel clinical score for predicting outcomes in stroke patients undergoing endovascular thrombectomy.MethodsThis retrospective study included all patients aged over 18 years with an anterior circulation stroke treated at a thrombectomy centre from 2010 to 2020. External validation data was obtained. The primary outcome variable was day 90 mRS ≥3. Existing clinical scores (SPAN and PRE) and Machine Learning (ML) models were compared. A novel clinical score (iSPAN) was derived by adding an optimised weighting of the most important ML features to the SPAN and compared results.Results812 patients were initially included (397 female, average age 73), 62 for external validation. The best performing clinical score and ML model were SPAN and XGBoost (sensitivity specificity and accuracy 0.967, 0.290, 0.628 and 0.783, 0.693, 0.738 respectively). A significant difference was found overall and XGBoost was more accurate than SPAN (p< 0.0018). The most important features were Age, mTICI and total number of passes. The addition of 11 points for mTICI of ≤ 2B and ≥ 3 points for 3 passes to the SPAN achieved the best accuracy and was used to create the iSPAN. iSPAN was not significantly less accurate than XGBoost (p>0.5). In the external validation set, iSPAN and SPAN achieved sensitivity, specificity, and accuracy of (0.735, 0.862, 0.79) and (0.471, 0.897, 0.67), respectively.ConclusionsiSPAN incorporates machine-derived features to achieve better predictions compared to existing scores. It is not inferior to the XGB model and is externally generalisable.Key PointsAn XGB model performed better than existing scores and other tested models for prognostication post EVT.It identified mTICI and number of passes as important and modifiable factors.Integrating these into the SPAN score (iSPAN) was not inferior to the XGB model and is generalisable and easier to use and interpret.
Data Quality of Out-Of-Pocket Payment on Institutional Delivery in India
Authors: Mohanty Sanjay K. , Dwivedi Laxmi Kant , Sharma Santosh Kumar , Ramesh Sowmya , Gautam Priyanka , Maiti Suraj , Nair Saritha , Singh S. K. 
Published: April 2023

AbstractEstimates of out-of-pocket (OOP) payment on health care are increasingly used in research and policy. In India, estimates of OOP payment are usually derived from health surveys carried out by the National Sample Survey (NSS). The questions on OOP payment on delivery care have recently integrated in the last two rounds of India’s National Family and Health Survey (NFHS-4 & NFHS-5). There are several issues relating to design of questions, reporting and recording of responses that have bearing on reliability of OOP estimates. This paper compares the OOP estimates from latest rounds of two of the large-scale population-based surveys; NFHS-5, 2019-21 and the National Sample Survey (NSS), 2018. We also highlight the type of question canvassed and its implications on OOP estimates of NFHS-5 survey. We used 155,624 births that were reported between in NFHS-5 and a total of 27,664 hospitalised cases for delivery care that were recorded in 75thround of NSS health survey, 2018. We have used descriptive statistics and two-part regression model to examine variations of OOP across surveys. We found large variations in distribution of OOP payment in NFHS-5 and NSS survey. Based on births during the five years preceding the survey, the OOP payment on institutional birth from public health centres in India from NFHS-5 was INR 2,894 (95% CI:2843-2945) compared to INR 2,738 (95% CI: 2644-2832) from NSS. Variations are similar for those availing services from private health centres. Controlling for socio-economic and demographic characteristics, the OOP payment from NFHS was lower among poorest and higher among richest compared to NSS. The variations in OOP across two surveys were larger across states of India. The variations in OOP payment across surveys were possibly due to structure of questions, recall bias, and variations in price level. We suggest to canvass standardised questions across surveys to obtain reliable OOP estimates across surveys.
‘Finding light in the darkness’: exploring comedy as an intervention for eating disorder recovery
Authors: Declercq Dieter , Kafle Eshika , Peters Jade , Raby Sam , Chawner Dave , Blease James , Foye Una 
Published: April 2023

AbstractPurposeEating disorders remain a major health concern with the incidence of these illnesses has increased since the Covid-19 pandemic. With increased demands on treatments and service provision, and evidence that waiting for treatment is harmful, it is important that research explores novel and innovative adjuncts within interventions for individuals experiencing eating disorders. There is growing evidence highlighting that arts’ interventions are beneficial for recovery from eating disorders, with comedy workshops specifically, have shown promising results for recovery.DesignThe study used a qualitative interview study design, utilising pre, post and three-month follow-up individual interviews and workshop observations, to explore the feasibility of conducting a comedy intervention for people in recovery from eating disorders (n=10).FindingsFindings show the comedy intervention had high acceptability and feasibility. For most individuals, participating in the course had positive outcomes, including promoting personal recovery across all five elements of the CHIME framework. Unique assets of the course included providing participants with an opportunity to distance themselves from everyday worries of living with an ED; the opportunity to cognitively reframe situations by making them the object of humour; and it provided a safe space to (re-)build a positive sense of self and develop self-acceptance through humour and play by challenging unhealthy perfectionism.OriginalityThis study highlights that such innovative approaches can positively support personal recovery for people with eating disorders, with findings providing evidence that this comedy intervention acts to address elements of the CHIME framework to encourage and enable positive outcomes among participants.
Family Research of Microbes Linked to Respiratory Infections (FAMILY Micro) observational study: Assessing the use of minimally invasive self-sampling methodologies at home for long-term monitoring of the oral, nasal and hand microbiota of adults and children within UK families
Authors: Nikolaou E. , German E.L. , Howard A. , Nabwera H.M. , Matope A. , Robinson R. , Shiham F. , Liatsikos K. , McNamara C. , Kattera S. , Carter K. , Parry C.M. , Read J.M. , Allen S.J. , Urban B.C. , Hawcutt D.B. , Hill H. , Collins A.M. , Ferreira D.M. 
Published: April 2023

AbstractBackgroundMonitoring the presence of commensal and pathogenic microorganisms in the human oral, nasal and hand niches as determinants for respiratory tract infections is of critical global relevance as was evident during the COVID-19 pandemic. However, community-based surveillance is difficult because current sampling methods are not optimal for a wide age range of participants, particularly young children. We designed a platform of minimally invasive self-sampling at home and assessed its use for longitudinal monitoring of the oral, nasal and hand microbiota of adults and children within families.MethodsHealthy families with two adults and up to three children, living in and near Liverpool, United Kingdom, self-collected saliva, nasal lining fluid using synthetic absorptive matrices and hand swabs at home every two weeks for six months. Questionnaires were used to collect demographic and epidemiological data and assess feasibility and acceptability. At the end of the study, participants were invited to take part in an interview.ResultsThirty-three families completed the study. Sample collection using our approach was acceptable to 25/33 (76%) families, as sampling was fast (76%), easy (76%) and painless (60%). Saliva and hand sampling was acceptable to all participants of any age, whereas nasal sampling was accepted mostly by adults and children older than 5 years.ConclusionMulti-niche self-sampling at home can be used by adults and children for longitudinal microbial surveillance, providing key data for monitoring respiratory infections.
Evidence for a role of human blood-borne factors in mediating age-associated changes in molecular circadian rhythms
Authors: Schwarz Jessica E. , Mrčela Antonijo , Lahens Nicholas F. , Li Yongjun , Hsu Cynthia T. , Grant Gregory , Skarke Carsten , Zhang Shirley L. , Sehgal Amita 
Published: April 2023

ABSTRACTAging is associated with a number of physiologic changes including perturbed circadian rhythms; however, mechanisms by which rhythms are altered remain unknown. To test the idea that circulating factors mediate age-dependent changes in peripheral rhythms, we compared the ability of human serum from young and old individuals to synchronize circadian rhythms in culture. We collected blood from apparently healthy young (age 25-30) and old (age 70-76) individuals at 14:00 and used the serum to synchronize cultured fibroblasts. We found that young and old sera are equally competent at initiating robust ∼24h oscillations of a luciferase reporter driven by clock gene promoter. However, cyclic gene expression is affected, such that young and old sera promote cycling of different sets of genes. Genes that lose rhythmicity with old serum entrainment are associated with oxidative phosphorylation and Alzheimer’s Disease as identified by STRING and IPA analyses. Conversely, the expression of cycling genes associated with cholesterol biosynthesis increased in the cells entrained with old serum. Genes involved in the cell cycle and transcription/translation remain rhythmic in both conditions. We did not observe a global difference in the distribution of phase between groups, but found that peak expression of several clock-controlled genes (PER3, NR1D1, NR1D2, CRY1, CRY2,andTEF) lagged in the cells synchronizedex vivowith old serum. Taken together, these findings demonstrate that age-dependent blood-borne factors affect circadian rhythms in peripheral cells and have the potential to impact health and disease via maintaining or disrupting rhythms respectively.
Design of effective personalised perturbation strategies for enhancing cognitive intervention in Alzheimer’s disease
Authors: Vohryzek Jakub , Cabral Joana , Perl Yonatan Sanz , Demirtas Murat , Falcon Carles , Gispert Juan Domingo , Bosch Beatriz , Balasa Mircea , Kringelbach Morten , Sanchez-Valle Raquel , Ruffini Giulio , Deco Gustavo 
Published: April 2023

AbstractOne of the potential and promising adjuvant therapies for Alzheimer’s disease is that of non-invasive transcranial neurostimulation to potentiate cognitive training interventions. Conceptually, this is achieved by driving brain dynamics towards an optimal state for an effective facilitation of cognitive training interventions. However, current neurostimulation protocols rely on experimental trial-and-error approaches that result in variability of symptom improvements and suboptimal progress. Here, we leveraged whole-brain computational modelling by assessing the regional susceptibility towards optimal brain dynamics from Alzheimer’s disease. In practice, we followed the three-part concept of Dynamic Sensitivity Analysis by first understanding empirical differences between healthy controls and patients with mild cognitive impairment and mild dementia due to Alzheimer’s Disease; secondly, by building computational models for all individuals in the mild cognitive impairment and mild dementia cohorts; and thirdly, by perturbing brain regions and assessing the impact on the recovery of brain dynamics to the healthy state (here defined in functional terms, summarised by a measure of metastability for the healthy group). By doing so, we show the importance of key regions, along the anterior-posterior medial line, in driving in-silico improvement of mild dementia and mild cognitive impairment groups. Moreover, this subset consists mainly of regions with high structural nodal degree. Overall, this in-silico perturbational approach could inform the design of stimulation strategies for re-establishing healthy brain dynamics, putatively facilitating effective cognitive interventions targeting the cognitive decline in Alzheimer’s disease.
A theory of oligogenic adaptation of a quantitative trait
Authors: Höllinger Ilse , Wölfl Benjamin , Hermisson Joachim 
Published: April 2023

AbstractRapid phenotypic adaptation is widespread in nature, but the underlying genetic dynamics remain controversial. Whereas population genetics envisages sequential beneficial substitutions, quantitative genetics assumes a collective response through subtle shifts in allele frequencies. This dichotomy of a monogenic and a highly polygenic view of adaptation raises the question of a middle ground, as well as the factors controlling the transition. Here, we consider an additive quantitative trait with equal locus effects under Gaussian stabilizing selection that adapts to a new trait optimum after an environmental change. We present an analytical framework based on Yule branching processes to describe how phenotypic adaptation is achieved by collective changes in allele frequencies at the underlying loci. In particular, we derive an approximation for the joint allele-frequency distribution at threshold levels of the trait mean as a comprehensive descriptor of the adaptive architecture. Depending on the model parameters, this architecture reproduces the well-known patterns of sequential, monogenic sweeps, or of subtle, polygenic frequency shifts. Between these endpoints, we observe oligogenic architecture types that exhibit characteristic patterns of partial sweeps. We find that a single compound parameter, the population-scaled background mutation rate Θbg, is the most important predictor of the type of adaptation, while selection strength, the number of loci in the genetic basis, and linkage only play a minor role.
The Brazilian COVID-19 vaccination campaign: A modelling analysis of socio-demographic factors on uptake
Authors: Li Sabrina L. , Prete Carlos A. , Zarebski Alexander E. , de Souza Santos Andreza A. , Sabino Ester C. , Nascimento Vitor H. , Wu Chieh-Hsi , Messina Jane P. 
Published: April 2023

AbstractThe COVID-19 pandemic has caused over half a million deaths in Brazil, and public healthcare nearly collapsed. Vaccination differs between states and demographics. Dose shortages delayed access.In this cross-sectional study, data were retrieved from the Brazilian Ministry of Health databases published since 17 January 2021, respectively. We developed a campaign optimality index to characterise inequality in vaccination access caused by age due to premature vaccination towards younger populations before older and vulnerable populations were fully vaccinated. We assessed geographical inequalities in full vaccination coverage and dose by age, sex, race, and socioeconomic status. Generalised linear regression was used to investigate the risk of death and hospitalisation by age group, socioeconomic status, and vaccination coverage.Vaccination coverage is higher in the wealthier South and Southeast. Men, people of colour, and low-income groups were more likely to be only partially vaccinated due to missing or delaying a second dose. Vaccination started prematurely for age groups under 50 years and may have hindered uptake of older age groups. Vaccination coverage was associated with a lower risk of death, especially in older age groups (OR: 10.5–34.8, 95% CI: (10.2, 35.9)). Risk of hospitalisation was greater in areas with higher vaccination rates due to higher access to care and reporting.Vaccination inequality persists between states, age and demographic groups despite increasing uptake. The association between hospitalisation rates and vaccination is attributed to preferential delivery to areas of greater transmission and access to healthcare.