dataesr/scientific-paragraphs-categorization huggingface dataset:
Spondyloarthritis Spondyloarthritis Spondyloarthritis between the reviewers were resolved via discussion or moderation by a third reviewer. A single reviewer performed data extraction of study characteristics and outcomes of the included studies. In cases where multiple publications reported results on the same cohort of patients or study, only the most recent and updated results were included. Data pertaining to study design, patient demographics (number, age, sex and employment status at study baseline), disease (type of axSpA, patient-reported activity limitations as measured by the Bath Ankylosing Spondylitis Functional Index and disease activity as measured by the Bath Ankylosing Spondylitis Disease Activity Index) and treatment characteristics and baseline WPAI domain scores from the included studies were extracted into tabulated Microsoft Excel worksheets.
Meta-analysis A random-effects meta-analysis was performed for baseline and mean change from baseline in WPAI domain scores following treatment with b/tsDMARDs or placebo. A random-effects (DerSimonian-Laird method) model was chosen to account for the expected heterogeneity across included studies. 21 22  Meta-analysis was run in the statistical program RStudio (R V. 4. 0. 2, Boston, Massachusetts, USA). The analysis included RCTs reporting WPAI outcomes at Weeks 12-16, as mean change or least-squares mean (LSM) change from baseline. Studies that did not report sample size, mean and SD for WPAI domain scores were included in the narrative synthesis but not included in the final model. Due to the homogeneity of various WPAI versions in structure and scoring, pooling and meta-analysis was deemed feasible.
Meta-analysis by subgroups: nr-axSpA versus r-axSpA At baseline, overall work impairment scores were reported for 289 employed patients with nr-axSpA and 705 employed patients with r-axSpA, with overall absolute mean scores of 48. 7% (95% CI: 44. 6% to 52. 8%) and 54. 2% (95% CI: 49. 9% to 58. 5%), respectively. Due to limitations in reporting of SDs, only data from the BE Spondyloarthritis Spondyloarthritis Spondyloarthritis MOBILE 1 study were included in the analysis of patients with nr-axSpA at Weeks 12-16. After treatment with b/ tsDMARDs, the absolute mean change from baseline in overall work productivity impairment at Weeks 12-16 was -26. 5% (95% CI: -32. 4% to -20. 7%) in patients with nr-axSpA (online supplemental figure S5) and -21. 1% (95% CI: -23. 1% to -19. 0%) in patients with r-axSpA (online supplemental figure  S6 ).
Strengths and limitations This study has several strengths. Our analyses were based on evidence retrieved through a systematic search of the literature with updates to account for the rapidly changing treatment landscape in axSpA. Standard electronic database search techniques were supplemented with cross-referencing SLRs and grey literature searches to identify all relevant data. Further, only WPAI domain scores reported in RCTs were included in the analyses. This study should be considered with the following limitations. This analysis focused only on studies that measured productivity using the WPAI questionnaire, a widely used and validated measure in axSpA. 13 14  Studies that evaluated the productivity outcomes using other tools (eg, Work Instability Scale for AS, Work Productivity Survey)  32 33  were excluded.
Considering that the WPAI is one of the six instruments identified by the Outcome Measures in Rheumatology Worker Productivity Group as a candidate for assessing at-work limitations of productivity loss,  34  and is consistent regardless of WPAI version, this measurement instrument enabled the comparison of findings across the included studies and the potential to compare results across all interventions. While a formal quality assessment was not performed, our analysis included only RCTs, which generally have a low risk of bias and high internal validity. We also acknowledge that the results from BE MOBILE 1 and 2 were not published at the time of this review and analysis and were therefore sourced from data on file, which may be considered as a potential source of bias. After the completion of this review and analysis, some of these results were presented as an abstract at ACR Convergence 2023.
Acknowledgements The authors also acknowledge  Celia Menckeberg  ( UCB Pharma ) for publication coordination and editorial assistance and  Megan Rutherford  and  Priya Ramakrishnan  ( Cytel ) for medical writing and editorial assistance based on the authors' input and direction. The authors also acknowledge  Costello Medical  for administrative and submission support. Contributors Substantial contributions to study conception/design, or acquisition/ analysis/interpretation of data: MR, MFM, BH, N-EZ, DW, VT and AB. Drafting of the publication, or revising it critically for important intellectual content: MR, MFM, BH, N-EZ, DW, VT and AB. Final approval of the publication: MR, MFM, BH, N-EZ, DW, VT and AB; as guarantor accepts full responsibility for the finished work and/or the conduct of the study, had access to the data, and controlled the decision to publish: MFM.
Data availability statement The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. The data for this study were sourced primarily from published literature. Data from BE MOBILE 1 (NCT03928704) and BE MOBILE 2 (NCT03895203) may be requested by qualified researchers 6 months after product approval in the USA and/or Europe, global development is discontinued or 18 months after trial completion. Investigators may request access to anonymised individual patient-.
MEDLINE, Embase, EconLit and Cochrane databases were searched for full-text articles published in English between 1 January 2010 and 21 October 2021 (online supplemental table S1). Relevant conferences including the American College of Rheumatology, EULAR, British Society for Rheumatology (BSR), Academy of Managed Care Pharmacy and The Professional Society for Health Economics and Outcomes Research were hand-searched between 1 January 2018 and 21 October 2021 for eligible studies. The full search strategy is provided in online supplemental table.
3) 48. 4 2. 7 (4. 2) 66 NR NR NR NR (NCT00939003) 38 40 mg Q2W Placebo 94 38 (10. 4) 42. 6 3. 0 (3. 8) NR NR NR NR TNFi, IL-17Ai COAST-V 2018 r-axSpA Adalimumab 90 42 (11. 4) 81. 0 7. 5 (7. 5) 74. 1 13. 6 (27. 2) 53. 2 (21. 9) 55. 0 (23. 3) 58. 2 (20. 5) (NCT02696785) 16 40 mg, Q2W/ Q4W Ixekizumab 81 41 (12. 1) 84. 0 8. 3 (9. 6) 8. 0 (20. 4) 57. 1 (21. 3) 58. 7 (21. 8) 58. 8 (18. 1) 80 mg, Q4W Placebo 87 43 (12. 0) 83. 0 6. 8 (7. 6) 7. 2 (14. 8) 61. 4 (19. 5) 63. 2 (20. 3) 62. 4 (20. 5) TNFi Huang 2014 r-axSpA Adalimumab 229 30 (8. 7) 80. 8 3. 0 (3. 8) NR NR NR 60. 0 (25. 3) 58. 8 (21. 7) (NCT01114880) 27 40 mg Q2W Placebo 115 30 (4. 5) 82. 6 3. 0 (3. 2) NR NR 67. 5 (24. 6) 61. 0 (21. 0) TNFi EMBARK 2020 nr-axSpA Etanercept 106 32 (7. 8) 64. 1 2. 4 (1. 9) 60 9. 1 (25. 0) 44. 9 (26. 7) 45. 0 (26.
2) 53. 2 (25. 9) (NCT01258738) 28 50 mg, QW Placebo 109 32 (7. 8) 56. 9 2. 5 (1. 8) 11. 8 (27. 7) 42. 2 (26. 3) 43. 9 (27. 8) 52. 1 (24. 3) IL-17Ai COAST-W 2019 r-axSpA Ixekizumab 114 47 (13. 4) 79. 8 10. 1 (7. 8) 57. 6 22. 9 (33. 2) 60. 0 (19. 7) 64. 0 (20. 8) 72. 5 (19. 6) (NCT02696798) 16 80 mg, Q4W Placebo 104 47 (12. 7) 83. 7 13. 0 (10. 5) 14. 6 (22. 0) 58. 8 (24. 5) 62. 3 (25. 7) 67. 5 (20. 1) IL-17Ai COAST-X 2021 nr-axSpA Ixekizumab 96 41 (14. 5) 52. 1 4. 2 (5. 5) 64. 7 † 12. 1 (27. 0) 50. 2 (22. 3) 51. 9 (22. 8) 63. 3 (21. 5) (NCT02757352) 39 80 mg, Q4W Placebo 105 40 (12. 4) 41. 9 3. 1 (4. 5) 15. 6 (26. 8) 62. 1 (18. 7) 65. 6 (20. 2) 66. 4 (20. 4) IL-17Ai MEASURE 1 2016 r-axSpA Secukinumab 125 40 (11. 6) 67. 0 6. 5 (6. 9) NR 11. 6 (21. 6) 45. 3 (24. 1) 49. 7 (26. 2) 56. 7 (23. 9) (NCT01358175) 25 150 mg Placebo 122 43 (12.
Linear Independent Component Analysis (ICA) is a blind source separation technique that has been used in various domains to identify independent latent sources from observed signals. In order to obtain a higher signal-to-noise ratio, the presence of multiple views of the same sources can be used. In this work, we present MultiView Independent Component Analysis with Delays (MVICAD). This algorithm builds on the MultiView ICA model by allowing sources to be delayed versions of some shared sources: sources are shared across views up to some unknown latencies that are view-and source-specific. Using simulations, we demonstrate that MVICAD leads to better unmixing of the sources. Moreover, as ICA is often used in neuroscience, we show that latencies are age-related when applied to Cam-CAN, a large-scale magnetoencephalography (MEG) dataset.
Related works Many ICA algorithms have been proposed to process data from multiple datasets at once. They vary by their assumptions: some methods assume that the datasets have the same dimension, while others consider that all datasets share the same sources. In this section, we describe some of these algorithms. MVICA  [Richard et al. , 2020]  is our reference algorithm. It is designed to identify shared independent components across multiple datasets. It assumes that the sources in each dataset are independent and that the shared sources have the same temporal patterns across datasets. Unlike some other multiview ICA approaches, MVICA's likelihood can be written in closed form and is then optimized with a quasi-Newton algorithm, using the gradient and an approximation of the Hessian of the likelihood.
, 2009]. Adding a back-reconstruction step is also possible to recover individual sources. However, this method does not bring statistical guarantees like consistency or asymptotic efficiency, contrary to MVICA. It also gives poorer results than MVICA on several neuroimaging tasks  [Richard et al. , 2020]. One strength of CCA-based ICA  [Varoquaux et al. , 2009; Tsatsishvili et al. , 2015]  is that they project multiple datasets onto a common underlying space by maximizing correlations between sets, which can be used for data fusion. Some of these methods can handle datasets with different numbers of sources. However, taking temporal delays into account in Group ICA would only be possible in the assembling step (done with PCA or multi-set CCA), which is less explicit than doing it during ICA optimization as we do in this paper.
Shared and Individual Multiview Independent Component Analysis  [Pandeva and Forré, 2022 ] can identify both shared and individual sources at the same time. This method assumes that the sources are independent within each dataset but allows for both shared and individual sources to contribute to the data. It uses a joint estimation approach to simultaneously estimate the shared and individual components and their corresponding spatial and temporal maps. However, the number of shared components needs to be specified, which can be difficult.
Cam-CAN dataset The Cam-CAN dataset  [Taylor et al. , 2017]  is a large and comprehensive dataset of neuroimaging, cognitive, and demographic data collected from a group of healthy adults. The dataset includes data from 661 participants, ranging in age from 18 to 88 years old, with equal numbers of participants in each 10-year age range. The participants were recruited from the general population in the Cambridgeshire area of the UK, with the aim of recruiting a sample that was representative of the local population in terms of age, sex, and education level. This dataset is freely available to researchers and has become a resource for investigating the neurobiological mechanisms underlying healthy aging and age-related cognitive decline. The Cam-CAN Stage 2 repository contains a subset of data from the larger Cam-CAN dataset, focused on the second wave of assessments conducted approximately two years after the initial assessment.
The MEG data in the Stage 2 repository includes recordings of brain activity using a whole-head 306 channel Elekta Neuromag Vectorview system. It contains data from several cognitive tasks, including visual and auditory tasks. For each of these tasks, participants were presented a series of stimuli, and averaging the obtained signals give what is called evoked data. In our experiments, we focus on evoked data of both tasks. We had to preprocess data and remove subjects with unusual signals, which reduced the number of subjects for the visual and auditory tasks. Also, these data last 0. 7 second at a sampling rate of 1000 Hz, thus giving datasets of shape  (477, 306, 701)  for the visual task and (501, 306, 701) for the auditory one.
Delay and age correlation The next experiment consists in studying age-delay correlation, as Cam-CAN data are often used to investigate mechanisms underlying healthy aging and age-related cognitive decline. Figure  5  shows the scatter plot of age and delay of the 477 subjects of the visual task. This scatter plot shows a significant correlation between the age of subjects and estimated delays. To produce it,  we first applied a PCA with 10 components on the dataset, whose shape became (477, 10, 701), and then used MVICAD to get estimated delays of shape (477, 10). We report here the source whose delays are most linearly related to age (using R 2 score, and p-value of Pearson correlation). From a biological point of view, having relevant and non-relevant sources in terms of age-delay correlation makes sense because some of the 10 obtained sources represent noise, whereas others are closer to the actual neural signals affected by aging.
ACKNOWLEDGEMENT Data collection and sharing for this project was provided by the Cambridge Centre for Ageing and Neuroscience (CamCAN). CamCAN funding was provided by the UK Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1), together with support from the UK Medical Research Council and University of Cambridge, UK. Data used in the preparation of this work were obtained from the CamCAN repository (available at http://www. mrc-cbu. cam. ac. uk/datasets/ camcan/). This work was supported by the ANR BrAIN (ANR-20-CHIA0016) grant.
Heat and mass transfers within the gaseous phase were analysed separately assuming spatially uniform temperatures and vapour concentrations at the surface of the droplets in the row. Corrections to the Nusselt and Sherwood numbers, accounting for the spacing between the droplets, were formulated in  [6]. https://doi. org/10. 1016/j. fuel. 2023  The model suggested in  [7]  was generalised by the authors of  [9]  to consider the contributions of multiple liquid components and relative diffusion between them. The same assumptions as in  [7]  were made in the new model except for that related to component diffusion. The analytical solutions to the equations for liquid fuel component diffusion were incorporated into the numerical code alongside the previously reported analytical solution to the temperature inside the composite droplet.
This generalisation (hereafter referred to as the Shift Model (SM)) was based on a purely numerical solution to the heat transfer equation in the fuel/water droplet using the finite element method with COMSOL multiphysics ®. In this analysis of puffing/microexplosion, the effects of mass loss due to fuel evaporation were ignored. The distance between the water/fuel interface and the outer surface of the fuel droplet was kept constant, which led to an overestimate of the time to puffing/micro-explosion. Also, the model described in  [8]  used the assumption that the fuel was a monocomponent fuel. In the present study, the focus is on an extension of the SM model to consider the multi-component nature of the fuel (diffusion between fuel components). In contrast to  [8] , the implementation of the model in the numerical code considers the changes in droplet sizes due to evaporation and thermal swelling.
This involves updates to the geometry of the composite droplet at each timestep of the calculations. A description of the new model and its implementation in the numerical code is presented in Section 2. The predictions of the model are compared with the predictions of the numerical code using the analytical solutions to heat transfer and component diffusion equations, developed by the authors of  [9] , in the case of a perfectly centred water subdroplet in Section 3. Estimates of the time to puffing/micro-explosion are made in the case of shifting of the water subdroplet for a wide range of ambient temperatures and droplet radii in Section 4. In Section 5, the predictions of the new model are compared with the results of experimental observations. For this comparison, two kerosene surrogates will be considered: • a two-component fuel mixture named SU12 composed of isoheptane and heptylbenzene with initial mass fractions 𝑌 𝑙10 = 0.
Implementation of the model The modules ''Transport of Diluted Species'' and ''Heat Transfer in Fluids'' in COMSOL multiphysics ® were used to numerically solve the heat transfer and component diffusion equations with appropriate initial and boundary conditions. There were some difficulties with satisfying the integral boundary conditions specified in Eqs. (  7 ) and (  14 ) due to the constraints of uniform temperature (𝑇 𝑠 ) and mass fraction (𝑌 𝑙𝑖𝑠 ) at the droplet surface. These difficulties were overcome by using the linearity of the heat and mass transfer problems. Both problems were decomposed into two sub-problems that were more straightforward to solve, with an imposed Dirichlet condition at the droplet surface. More details are provided in Appendix. In contrast to  [8] , the time variations of 𝑅 𝑑 (𝑡) and 𝑅 𝑤 (𝑡) are taken into account in the numerical resolution.
The following parameter values were set in this comparison: • Initial droplet temperature 𝑇 𝑑0 = 300 K. • Initial water volume fraction 10%. • Ambiant pressure fixed at 𝑃 = 101 325 Pa. Figs. 2  and 3  show the time evolution of the temperatures at the droplet's surface (𝑇 𝑠 ) and the water/fuel interface (𝑇 𝑤 ) along with the mass fraction of the fuel components (𝑌 𝑙𝑖𝑠 ) at the droplet surface for both SU12 and SU1. Calculations were performed for ambient gas temperature of 573 K and initial radius of the composite droplet equal to 1 mm. As follows from Figs. 2  and 3 , the results for both 'a-CM' and 'n-CM' coincide within the accuracy of plotting. This suggests that the code based on the numerical solutions to the heat transfer and component diffusion equations described in Section 2 and the code based on the analytical solutions to these equations are verified for this case.
Case The location of the water subdroplet inside the fuel droplet was identified before the start of heating. Special care was taken to evaluate the shifting of the water subdroplet in the experiments, which was achieved using a combination of planar laser-induced fluorescence (PLIF) and shadowgraphy techniques  [8]. The PLIF techniques allowed us to illuminate the water subdroplet. Shadowgraphy allowed us to illuminate both water subdroplet and fuel shell. The centres of the water subdroplet and fuel shell were determined using an in-house image analysis programme based on Matlab software and its image processing toolbox (see  [8]  for the details). During experiments, the location of the water subdroplet was not controlled. When applying the new model to the analysis of experimental data, effects of forced convection had to be taken into account due to gas velocities in the range 2.
The newly developed model was verified based on the comparison of its predictions with those of the previously developed numerical code based on the analytical solutions to the heat transfer and component diffusion equations, used at each timestep of the calculations, for the case of a perfectly centred water droplet. The start of the puffing/micro-explosion process was associated with the time instant when the temperature at the point of the water/fuel interface closest to the surface of the droplet reached the nucleation temperature of water. The coincidence of the results supported both approaches to the problem. The timing of puffing/micro-explosion was then evaluated for various positions of the water subdroplet. Shifting the water subdroplet location resulted in a reduction in the time to puffing/micro-explosion, which was particularly noticeable for high gas temperatures and large normalised shifts.
Implementation in the numerical code The mesh in the numerical solution was composed of about 250,000 structured quadrangle elements in the fuel region of the computational domain, and 10,000 unstructured triangular elements in the water domain. The numbers of elements varied slightly when updating the geometry and the mesh to account for thermal swelling and mass loss of components due to evaporation. The mesh was refined near the outer surface of the droplet in order to capture the gradients of component mass fractions which are expected to be large at the short times in this region. The solution was performed using MATLAB code which calls COMSOL to solve the partial differential equation (PDE) in the problem described above. An iterative procedure was implemented to ensure the required precision of the results at each timestep.
Acknowledgements The authors are grateful to the  Tomsk Polytechnic University ( TPU) development program ,  Priority 2030  ( Priority-2030-NIP/EB-038-1308-2022 ) which supported  D. Antonov ,  P. A. Strizhak , and  S. S. Sazhin , and to the  Russian Science Foundation  (Grant  21-19-00876 , https://rscf. ru/ en/project /21-19-00876/ ) which supported  I. Zubrilin.
Background: High-fidelity (HF) pediatric patient simulators are expensive. This randomized study aimed to compare the quality and educational impact of a full-scale simulation workshop with an HF infant simulator (SimBaby TM , Laerdal) or with a low-cost (LC) simulator composed of an inert infant manikin with SimBaby TM software that displays respiratory/hemodynamic parameters on a monitor for medical education in pediatric difficult airway management. Methods: After written informed consent, anesthetists and emergency or ICU physicians participated in teams (4 to 6 participants) in a training session that included direct participation and observation of two difficult intubation scenarios. They were randomized into two groups (HF group, n = 65 and LC group, n = 63). They filled out a simulation quality score (SQS, 0 to 50), self-evaluated their anesthetists' non-technical skills (ANTS) score (15 to 60), and an educational quality score (EQS, 0 to 60) immediately (T0, main criteria), as well as 3 (T3) and 6 (T6) months after the training session.
Introduction Tracheal intubation is frequently performed in children in anesthesia, intensive care, and emergency medicine. Despite the development of guidelines, 1 difficult intubation remains a major cause of morbidity 2 owing to the physiological characteristics of infants and young children, thereby contributing to the rapid onset of hypoxia during apnea. 3 he incidence of laryngospasm is especially increased when the anesthetist is insufficiently experienced. 4 Continuing education in difficult intubation is more necessary since it is less common in children. 5 High-fidelity simulation (HF) is a positive contribution to the management of pediatric emergencies with scenarios that recreate the clinical context as closely as possible. 6---8 HF patient simulators such as the SimBaby TM (Laerdal, Stavenger, Norway) include electromechanically controlled systems with computer software in order to mimic physiological functions including respiratory, hemodynamic, and pathological events.