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Researchers’ perspectives on methodological challenges and outcomes selection in interventional studies targeting medication adherence in rheumatic diseases: An OMERACT-Adherence study
No full textBackground: Research on adherence interventions in rheumatology is limited by methodological issues, particularly heterogeneous outcomes. We aimed to describe researchers’ experiences with conducting interventional studies targeting medication adherence in rheumatology and their perspectives on establishing core outcomes.Methods: Semi-structured interviews using audio conference were conducted with researchers who had conducted an adherence study of any design in the past 10 years. Data collection and thematic analysis were performed iteratively, until saturation. Results: We interviewed 13 researchers, most of whom worked in academia and specialized in epidemiology and/or health services research. We identified three themes: 1) improving measurement of adherence (considering all phases of adherence, using appropriate and relevant measures, and establishing clinically meaningful thresholds); 2) challenges in designing and appraising adherence intervention studies (considering the confusion over a plethora of outcomes, difficulties with powering studies to demonstrate meaningful changes, and suboptimal descriptions of adherence interventions in published studies); and 3) advancing outcome assessment in adherence intervention studies (capturing rationale for developing a core domain set as well as recommendations and anticipated challenges by participants). Conclusions: Uniquely gathering perspectives from international adherence researchers, our findings led to researcher-informed recommendations for improving adherence research including specifying the targeted adherence phase in designing interventions and studies and providing a glossary of terms to promote consistency in reporting. We also identified recommendations for developing a core domain set for interventional studies targeting medication adherence including involvement of patients, clinicians, and other stakeholders and methodological and practical considerations to establish rigor and support uptake. <br/
Laboratory Safety of Dupilumab in Patients Aged 6 to < 12 Years with Severe Atopic Dermatitis: Results from a Phase 3 Clinical Trial
No full textBackground: Previous studies of dupilumab in adolescents and adults with moderate-to-severe atopic dermatitis (AD) showed no clinically meaningful adverse changes in laboratory parameters. Objective: To assess laboratory outcomes in children aged 6 to < 12 years with severe AD in a randomized, placebo-controlled, phase 3 trial of dupilumab. Methods: Children aged 6 to < 12 years with severe AD were randomized 1:1:1 to 16 weeks of dupilumab 300 mg every 4 weeks, 100 or 200 mg every two weeks, or matching placebo, all with concomitant topical corticosteroids (TCS). Blood samples were collected at baseline and Weeks 4, 8, and 16; urine samples were collected at baseline and Weeks 4 and 16. Results: Of 367 patients enrolled in the study, 362 were included in the safety analysis, 351 completed study treatment, and 4 withdrew due to treatment-emergent adverse events not related to laboratory abnormalities. Bot dupilumab + TCS groups showed overall trends toward increases in mean blood levels of eosinophils and alkaline phosphatase, and decreases in mean blood levels of platelets, neutrophils, and lactate dehydrogenase levels, without corresponding mean changes in the placebo + TCS group. None of these changes were associated with symptoms or clinically meaningful adverse outcomes, and none led to treatment modification. No clinically significant changes or trends were observed for other measured laboratory parameters. Conclusion: There were no clinically meaningful adverse changes in routine laboratory parameters attributable to treatment with dupilumab + TCS. Changes in platelet counts and lactate dehydrogenase levels likely reflect reduced inflammation. These results confirm similar findings in adults and adolescents, and suggest that there is no need for routine laboratory monitoring of children aged 6 to < 12 years treated with dupilumab + TCS for severe AD
Student, academic and professional services staff perspectives of postgraduate researcher wellbeing and help-seeking: a mixed-methods co-designed investigation
No full textPurpose This study aimed to address three key gaps in existing knowledge about postgraduate researchers’ (PGRs) wellbeing. It investigated the (i) frequency and nature of depression, anxiety, and wellbeing amongst PGRs, and relatedly, characteristics that convey vulnerability (ii) factors that impact PGR wellbeing, and (iii) factors that influence help-seeking.Design/methodology/approach The mixed-methods design comprised quantitative and qualitative approaches. Using opportunity sampling, 585 PGRs registered at a large UK University completed an online survey. The perspectives of a purposive sample of academic and Professional Services staff (n = 61) involved in supporting PGRs were sought through in-depth focus groups and semi-structured interviews, which were audio-recorded, transcribed verbatim, and analysed using inductive thematic analysis.FindingsPGRs scored lower on measures of wellbeing and higher on measures of anxiety and depression compared with aged-matched groups in the general population. PGR wellbeing was positively affected by personal and professional relationships, and negatively affected by academic challenges and mental health problems. Academic supervisors were the primary source of support for students experiencing wellbeing difficulties. Thematic analysis revealed four domains that impact upon PGR wellbeing: postgraduate researcher identity; pressures and expectations of postgraduate research; complexity of the supervisor role; and pinch points in postgraduate research. Each domain had associations with help-seeking behaviours. Originality/value This study provides evidence that the PGR experience is perceived to be distinct from that of other students and this helps understand sources of stress and barriers to help-seeking. It provides a steer as to how higher education institutions could better support the PGR learning experience. <br/
Design and optimization of a TensorFlow Lite deep learning neural network for human activity recognition on a smartphone
No full textHuman Activity Recognition (HAR), using machine learning to identify times spent (for example) walking, sitting, and standing, is widely used in health and wellness wearable devices, in ambient assistant living devices, and in rehabilitation. In this paper, a stacked Long Short-Term Memory (LSTM) structure is designed for HAR to be implemented on a smartphone. The use of an edge device for the processing means that the raw collected data does not need to be passed to the cloud for processing, mitigating potential bandwidth, power consumption, and privacy concerns. Our offline prototype model achieves 92.8% classification accuracy when classifying 6 activities using a public dataset. Quantization techniques are shown to reduce the model’s weight representations to achieve a >30x model size reduction for improved use on a smartphone. The end result is an on-phone HAR model with accuracy of 92.7% and a memory footprint of 27 KB
Full Slonczewski-Weiss-McClure parametrization of few-layer twistronic graphene
No full textWe use a hybrid k p theory - tight binding (HkpTB) model to describe interlayer coupling simultaneously in both Bernal and twisted graphene structures. For Bernal-aligned interfaces, HkpTB is parametrized using the full Slonczewski-Weiss-McClure (SWMcC) Hamiltonian of graphite1, which is then used to refine the commonly used Bistritzer-MacDonald (BM) model for twisted interfaces2,3, by deriving additional terms that reflect all details of the full SWMcC model of graphite. We find that these terms introduce electron-hole asymmetry in the band structure of twisted bilayers, but in twistronic multilayer graphene, they produce only a subtle change of moir´e miniband spectra, confirming the broad applicability of the BM model for implementing the twisted interface coupling in such systems
Poor sleep behavior burden and risk of COVID-19 mortality and hospitalization:United Kingdom, March—December 2020
No full textThe presence and characteristics of ‘spin’ among randomized controlled trial abstracts in orthodontics
No full textObjectives: To identify the presence and characteristics of spin (using reporting strategies to distort study results and mislead readers) within randomized controlled trial (RCT) abstracts published in orthodontic journals, and to explore the association between spin and potentially related factors.Methods: A manual search was conducted to identify abstracts of RCTs with statistically nonsignificant primary outcomes published in 5 leading orthodontic journals between 2015 and 2020. Spin in the Results and Conclusions sections of each included abstract was evaluated and categorized according to pre-determined spin strategies. Logistic regression analyses were employed to explore the association between spin and relevant factors.Results: A total of 111 RCT abstracts were included, of which 69 (62.2%) were identified with spin. In the Results section, 47 (42.3%) abstracts had spin, and ‘focusing on significant withingroup comparison for primary outcomes’ was the most frequent spin strategy. In the Conclusions section, 57 (51.4%) abstracts presented spin, with the most common strategy being ‘claiming equivalence or non-inferiority for statistically nonsignificant results’. According to multivariable logistic regression analysis, a significantly lower presence of spin was found in studies with international collaboration (OR: 0.331, 95% CI: 0.120 to 0.912, P = 0.033) and trial registration (OR: 0.336, 95% CI: 0.117 to 0.962, P = 0.042).Conclusion: The prevalence of spin is high among RCT abstracts in orthodontics. Clinicians need to be aware of the definition and presence of spin. Concerted efforts are needed from researchers and other stakeholders to address this issue
Screening, diagnostic and prognostic tests for COVID-19: A 2 comprehensive review
No full textWhile molecular testing implementing real-time polymerase chain reaction (RT-PCR) re-22 main the gold-standard test for COVID-19 diagnosis and screening, more rapid or affordable mo-23 lecular and antigen testing options are developed. More affordable, point-of-care antigen testing, 24 despite being less sensitive compared to molecular assays, it might be preferable for wider screening 25 initiatives. Simple laboratory, imaging and clinical parameters could facilitate prognostication and 26 triage. This comprehensive review summarizes current evidence on the diagnostic, screening and 27 prognostic tests for COVID-19
On The Relationship Between Differential Algebra and Tropical Differential Algebraic Geometry
No full textThis paper presents the relationship between differential algebra and tropical differential algebraic geometry, mostly focusing on the existence problem of formal power series solutions for systems of polynomial ODE and PDE. Moreover, it improves an approximation theorem involved in the proof of the fundamental theorem of tropical differential algebraic geometry which permits to improve this latter by dropping the base field uncountability hypothesis used in the original version
Penalized joint generalized estimating equations for longitudinal binary data
No full textIn statistical research, variable selection and feature extraction are a typical issue. Variable selection in linear models has been fully developed, while it has received relatively little attention for longitudinal data. Since a longitudinal study involves within-subject correlations, the likelihood function of discrete longitudinal responses generally can not be expressed in analytically closed form, and standard variable selection methods can not be directly applied. As an alternative, the penalized generalized estimating equation is helpful but very likely results in incorrect variable selection if the working correlation matrix is misspecified. In many circumstances, the within-subject correlations are of interest and need to be modeled together with the mean. For longitudinal binary data, it becomes more challenging because the within-subject correlation coefficients have the so-called Frechet-Hoeffding upper bound. In this paper, we proposed SCAD-based and LASSO-based penalized joint generalized estimating equation (PJGEE) methods to simultaneously model the mean and correlations for longitudinal binary data, together with variable selection in the mean model. The estimated correlation coefficients satisfy the upper bound constraints. Simulation studies under differentscenarios are made to assess the performance of the proposed method. Compared to existing penalized generalized estimating equation (PGEE) methods that specify a working correlation matrix for longitudinal binary data, the proposed PJGEE method works much better in terms of variable selection consistency and parameter estimation accuracy. A real dataset on Clinical Global Impression is analyzed for illustration