dataesr/scientific-paragraphs-categorization huggingface dataset:
23, 24 Discarding eligibility failures and incomplete datasets, 931 ARCA/EOA patients provided SARA and/or phenotypic data for the current study. The total cohort included overall 1,637 SARA assessments in 884 patients (370 with 2-8 longitudinal assessments; 86 genotypes), at least one INAS assessment in 908 patients, 187 assessments with FARS-FS, and 62 assessments with FARS-ADL. As a general structuring principle, analyses were performed on this total cohort by default, and in genotypespecific subcohorts depending on available datasets. If genotype-specific data were too limited to allow for robust analysis, analyses were performed on a subset of 9 ARCAs recurring in at least 15 patients grouped and referred to as "common ARCAs" (n = 393 patients), with adjustment for genotype as covariate in the respective multivariate statistical analysis.
4 (procedure MIXED, NLMIXED). SPSS 25 (IBM, Armonk, NY) was used to perform GLMs (function GENLIN). Correlations, curve fitting, descriptive, and confirmatory statistics were calculated with Prism 9 (GraphPad Software, La Jolla, CA), using nonparametric measures (Spearman rho, median, interquartile range (IQR)). MATLAB 2019b (The MathWorks, Natick, MA) was used to determine bootstrapped 95% CIs of Spearman correlations. between SARA subitem and total SARA (equal to overall ataxia severity). Response curves and 95% prediction intervals were estimated by third order polynomial curve fits. SARA subitems show ceiling effects (gait, stance), floor effects (sitting), and nonresponsive "static" periods at moderate ataxia severity (particularly speech, finger-chase, and nose-finger). Distribution of subitem score levels, depicted in the vertical bar charts next to each subitem, shows considerable ceiling effects (gait, stance) and floor effects (sitting), as well as under-representation of intermediate scores ( gait, stance, and sitting, but also uneven scores for appendicular items) and high scores (speech, finger-chase, nose-finger).
Longitudinal Natural History Data Our study provides natural history data for several ARCAs based on the SARA, informing on annualized progression for trial size estimations (mean and variability of slope) and ataxia severities that need to be expected upon trial planning (distribution of intercepts). Specifically, we provide for the first time natural history data for AOA2, SYNE1-ataxia, and AT. Albeit preliminary, given the limited number of observations and heterogeneous time courses, such data are urgently required for trial planning, as treatments are on the horizon for several of these ARCAs, either for the whole ataxia disease type  37  or for individual patients thereof susceptible to individualized genetic treatments. 38 Our longitudinal data partly confirm, partly revise earlier results on ARCA progression estimates from a smaller, European, and cross-sectional only cohort.
Acknowledgments This work was supported via the European Union's  Horizon 2020 research and innovation program  as part of the innovation project  EVIDENCE-RND  under the  EJP RD COFUND-EJP  (No. 825575 , to R. -D. H. and M. S. ), as part of  Solve-RD  (No. 779257 , to J. B. , M. S. , and B. P. v. d. W. ), by the  DFG  under the framework of  EJP-RD network PROSPAX  (No. 441409627 ; M. S. , B. P. v. d. W. ), and by the  Clinician Scientist program PRECISE. net, funded by the  Else Kröner-Fresenius-Stiftung  (to A. Tr. and M. S. ). The study was further funded by the  Federal Ministry of Education and Research, Germany , and through the  TreatHSP network  ( 01GM1905  to L. S. ). B. P. v. d. W. receives additional research support from  ZonMW ,  Hersenstichting ,  Gossweiler Foundation , and  Radboud University Medical Center.
L. S. , T. Klop. , T. Kloc. , G. Z. , B. P. v. d. W. , and M. S. are members of the European Reference Network for Rare Neurological Diseases-Project  ID  No. 739510. A. Tr. receives funding from the  University of Tübingen , medical faculty, for the Clinician Scientist Program Grant # 439-0-0. P. K. receives funding from  University of Szeged  ( Hetényi Géza :  5S330 A202 ) and  Ministry of Innovation and Technology of Hungary, National Research, Development and Innovation Fund  ( TKP2021-EGA ). J. B. is supported by a  Senior Clinical Researcher mandate of the Research Fund-Flanders (FWO)  under grant agreement number  1805021N , and is a member of the μNEURO  Research Center of Excellence of the University of Antwerp. F. M. S. is supported in part by the  Italian Ministry of Health  ( EJP-RD network PROSPAX ;  Ricerca Finalizzata   RF-2016-02361610 ;  RF-2019-12370417 ;  Ricerca Corrente   RC 5x1000 ).
, 2019). In order to prevent hydrostatic non-equilibrium, contact materials between the sample and the porous plate are frequently used to improve the contact  (Klute, 1986; Reynolds and Topp, 1993). The effects of these procedural aspects are not clearly established  (Gee et al. , 2002; Gubiani et al. , 2013). The methods that have been used, up to date, to measure the SWRC are different between laboratories, leading to nonharmonized datasets. Also, procedures for the same method differ from one laboratory to another. As a consequence, most SWRC databases that are used to create pedotransfer functions and maps pool non harmonized data from different laboratories  (Wösten et al. , 1999; Nemes et al. , 2001; Weynants et al. , 2013; Tóth et al. , 2015 Tóth et al. , , 2017)). It is argued that an important source of uncertainty of pedotransfer functions comes from the uncertainty of measured input data and that the standardization of experimental protocols could significantly enhance their quality  (Vereecken et al.
The data analysis The final data set consisted of 250 SWRCs. Two curves were missing. Since each SWRC was composed of four successive measurement points whose relative value may depend on the previous point, the data were not independent. Statistical analyses were then based on parameter values of fitted functions. The measured wet part of SWRCs was adjusted to a linear function with log 10 (hP a) = pF as the independent variable and was used to model our dataset (Eq. 1). wc i = β 0 + (β 1 * (pF i -1)) + ϵ i (1) Water content (wc i ) was linearly expressed as a function of pF values (pF i = 1. 00, 1. 70, 2. 00, 2. 48). The pF i -1 was intended to set the first retention point at the intercept. The index i represented the i th data point. wc in = β 0 + z 0n varying intercept + ((β 1 + z 1n ) * (pF i -1)) varying slope +ϵ in (2) The n index represented the n th SWRC.
Improving and standardizing procedures requires a full assessment of the effect of each step of the procedures on the final SWRC measurement. Harmonization of methods can be achieved with interlaboratory comparisons. Nevertheless, this requires that the reference samples remain stable over time, which was not the case in this study. Further work is needed to design new reference samples (e. g. with clays, sintered glass beads or ceramics) that can be used by laboratory as internal and external quality control. Since procedures and methods could have an impact on the final measurement of the SWRC, the transparency of the procedures and methods used in SWRCs datasets should be ensured. These recommendations aim to contribute to the improvement of knowledge of hydrological processes and to the consistency of databases built on multiple laboratories' inputs, such as those used to derive the most widely used pedotransfer functions.
Results The simulation results obtained with a uniform and two non-uniform slip models generally show good agreement with the recorded data either by the coastal gauges or the DART stations in the southwest Pacific region. A deep look at the results is necessary to highlight the differences and similarities between the three models. The results obtained with a maximum plausible Mw 8. 2 scenario are presented afterward. The non-uniform slip models (scenario #2 and scenario #3) show generally quite similar waveforms, scenario #3 being most of the time smaller than scenario #2 in terms of amplitude.
Coastal gauge records It is noticeable that the models are not able at all to reproduce the real signal at one location: VANU (Port Vila, Vanuatu) although numerous tests have been done to try to fit it correctly: changing the location of the virtual gauge, smoothing the bathymetric data or increasing its resolution. The other differences are related to the de-tiding method of the real signals using a polynomial fitting that is not always able to remove the whole components of the tide or to meteorological conditions like storm surges producing low frequency waves (e. g. SPRG and CHIT). These comparisons need to be considered cautiously with regards to the overall small amplitude of the tsunami. But globally, scenario #2 presents a good compromise between the two other scenarios, being able to satisfy both near and far-field expectations.
DART records Simulated sea level fluctuations due to tsunami waves at DART C, E, G and I location for each slip model are compared to real DART records on Figure  10. The reader must consider that the available 15 s sampling rate record transmitted in real-time by the BPR to the monitoring centre stops at 17:00 for DART E and stops at 18:30 for DART C, G and I. In terms of arrival time, the three scenarios show good visual agreement with the records for the four stations. In terms of periodicity on each station, scenario #1 produces a leading wave period longer of 3-4 minutes than the records, leading to a phase shift of the wave train. On DART C and E, scenario #2 provides the best match with recorded data in terms of arrival time and first wave amplitude and periodicity. A time shift of ~2 min occurs in the first through (after the leading wave arrival) and is reflected on the following waves, which is not the case with the scenario #3, fitting better the oscillations coming after the first wave.
Comparison of the slip models results The tsunami modelling results show that both uniform slip model built from CMT solution (scenario #1) and non-uniform slip models calculated from tsunami waves inversion (scenario #2) or seismic data (scenario #3) are able to reproduce the recorded signal of the small tsunami following the 10 February 2021 Mw 7. 7 thrust event generated at the southeasternmost part of the VSZ on most of the 24 coastal gauges and 4 DART stations of the southwest Pacific region considered in this study. This reproduction shows differences in some locations that can be attributed either to the resolution of the grids directly linked to the available bathymetric data quality, or to the dispersion phenomenon affecting the tsunami waves during propagation over long distances, or to the quality of the real coastal gauge data (including possible time and vertical offsets) or finally to the initial assumption on the source geometry used in tsunami inversion process.
projects and could be obtained under specific conditions (contact corresponding author for more information). The earthquakes (https://earthquake. usgs. gov), centroid moment tensors (https://www. globalcmt. org), coastal gauge records (https://www. linz. govt. nz/sea/tides/sea-leveldata/sea-level-data-downloads; http://www. ioc-sealevelmonitoring. org) and New Zealand DART data (https://www. geonet. org. nz/tsunami/dart) are publicly available.
prepared, covering the whole southwest Pacific region from 140 to 200°E and 0 to 50°S (first level grid 01) and specific areas (second level and its sub-level grids) focusing on each coastal gauge and DART station having recorded the 10 February 2021 tsunami and used in this study. Digital Elevation Models (DEM) used for these grids have been built from different datasets within the framework of previous projects. Norfolk Island high-resolution DEM has been specifically built for this study. The first level (grid 01) is at the lowest resolution (2 arc-min) covering the whole southwest Pacific region; its data comes from the ETOPO 1 global dataset (Amante and Eakins, 2009)  with some refinements around New Zealand. The second level of grids, showing higher resolution of 30 to 24 arc-sec (~930 and 740 m respectively, cover several sub-regions focusing on New Zealand (grid 02), New-Caledonia/south Vanuatu (03), Norfolk Island (04), Australia east coast (Gold Coast -05 and New South Wales -06), Tasmania (07), Fiji (08), Raoul Island (09), Tonga (10), Samoa (11) and Tuvalu (12).
Then, depending on the availability of higher resolution data, there is either one or two additional sub-level grids with https://doi. org/10. 5194/nhess-2022-157 Preprint. Discussion started: 7 June 2022 c Author(s) 2022. CC BY 4. 0 License. increasing resolution toward the area where a coastal gauge is located. The extent of most of the grids is presented on Figure 8. The resolution of each sub-level grid is calculated by COMCOT based on an input grid size ratio to the resolution of the previous level grid. The highest resolution used in this study is ~10 m in places where the bathymetry and the coastal shape is very complicated like Lenakel (Tanna Island, Vanuatu), as even minor inaccuracies in how these areas are represented could lead to very inaccurate results. For places like Tonga, Fiji, Tuvalu and Samoa where high-resolution dataset was not.
Design and setting A qualitative approach was used to address the study aims. We chose qualitative inquiry as we wished to hear participants describe their experiences in enough detail to enable a comprehensive understanding of the interplay between family visitation policies and clinician experience. Focus groups were chosen as a method of data collection as the interaction between those involved is thought to help participants consider and reflect upon aspects of daily life (and work) that are sometimes taken for granted  [15]. Clinicians were recruited from three international sites to understand if staff experiences differed across international contexts. Details of the international policy related to hospital visitation are provided in Table  1. Interviews were undertaken in the local language and translated into English.
Participants were recruited through advertising this research in staff areas and via personal solicitations; staff interested in participating were then given the option to attend a focus group. We aimed to recruit a diversity of clinicians to fully understand the impact of family visiting restrictions across the entire MDT. Focus groups were facilitated by experienced researchers and were undertaken in-person (in the hospital setting) and virtually. Researchers involved in focus group facilitation had extensive experience of qualitative data collection. Within this analysis, data saturation was defined as the point at which linking the concepts of two consecutive focus groups revealed no additional insights  [16]. This was determined by the research team via iterative discussion across international sites.
No waiting room Declarations Ethics approval and consent to participate UK: This study was approved by the Scientific Officer of the  West of Scotland Research Ethics Service as Service Evaluation (UK approval ); the  Hospital Clínic of Barcelona Medical Research Ethics Committee  ( HCB/2021/1115 ) (Spain approval); and the research ethics board  Sud Méditerranée  ( 2020-A00809-30 ;  CNRIPH: 20. 03. 27. 73019 ) within the framework of the BURDENCOV study (France approval). Competing interests JM is funded via a Fellowship from  The Healthcare Improvement Studies Institute (University of Cambridge  ( PD-2019-02-16 ). Dr Azoulay  reported receipt of personal fees (lectures) from  Pfizer ,  Gilead ,  Baxter , and Alexion; and institutional research grants from  Merck Sharp and Dohme ,  Pfizer ,  Baxter , and Alexion outside the submitted work.
Background The development of e-health technologies for teleconsultation and exchange of knowledge is one of the core purposes of European Reference Networks (ERNs), including the ERN EURO-NMD for rare neuromuscular diseases. Within ERNs, the Clinical Patient Management System (CPMS) is a web-based platform that seeks to boost active collaboration within and across the network, implementing data sharing. Through CPMS, it is possible to both discuss patient cases and to make patients' data available for registries and databases in a secure way. In this view, CPMS may be considered a sort of a temporary storage for patients' data and an effective tool for data sharing; it facilitates specialists' consultation since rare diseases (RDs) require multidisciplinary skills, specific, and outstanding clinical experience.
In short, case discussions happen through the opening of so-called "panels", where the clinician requiring a medical consultation provides a detailed description of clinical case. Data sharing (e. g. radiological images, exams reports, laboratory findings) via panels is possible, so that advice on virtual consultation can be easily provided by the specialists invited both using the chat tool and via video meetings. An outcome that summarizes all steps of case discussion and all conclusive remarks is produced, and patients' data is available to be used in databases and registries, should the patient agree in giving consent. In this view, CPMS is more than just a platform for case solving: thanks to its ability to store a high amount of data, it is a real digital database, effective for research and data analysis.
It may be considered as an electronic health record/database that keeps all patients data and increases the evidence-based knowledge of rare diseases. It achieves ultra-rare disease sharing and accurate diagnostic as well, and facilitates access to genetic testing and other biochemical testing. Since any functional (clinical, phenotypic, imaging, biochemical, genetic, etc. ) study carried out can be reported in the platform, it may serve as a source of functional data providing unprecedented scientific insights as well. Therefore, this growing database will potentially be adopted to support data interpretation as those related to Variant of Uncertain Significance (VUS)  [12]. The outstanding added value of CPMS is the capability to let HCPs from all ERNs crosstalk and share cases, therefore establishing a multidisciplinary clinical and diagnostic context, where achieving the correct diagnosis is maximized.
Methods The consent process for patients was established by DG SANTE in collaboration with the legal and ethical working group of the ERNs. A common information and consent form, complying with General Data Protection Regulation (GDPR), was translated in 24 EU languages and is available to be used across all ERNs. The local ethical committee approval for each participating centre is not mandatory since the CPMS has obtained a European centralised ethical approval, and therefore EU countries participating to CPMS automatically accepted this approval. The informed consent process for patients was led by healthcare professionals (geneticists, neurologists, pediatric neuropsychiatrists) who are responsible for enrolling patients in CPMS. The healthcare professional explained CPMS contents, uses, and ethical issues during routine consultation and illustrated the consent form which is composed of three "sessions": (i) consent to share de-identified data in ERN(s) for care to discuss the case in CPMS (obligatory); (ii) consent to include de-identified data in one or more ERN databases or registries (optional); (iii) consent to be contacted about research in the future (optional).
This strategy was successful in increasing the number of registered users on the platform among the participants to the course from four to 32 and in multiplying by a factor five the number of panels eventually opened (Fig. 1 ). Differences in HCP proficiency in the use of the system and differences in number of patients available are reflected by the number of panels that had been opened across different centers, going from three to 14 (Fig. 1 ). We cannot provide numeric data about those patients' who have been already discussed and diagnoses/treatments already achieved. Indeed, since only 12 panels were closed/signed-off, being this a minority of all discussed cases (98), it is not possible to drive general numerical or data quality conclusions about the outcomes of our CPMS panels. As far as we know, this is the first hands-on training designed to promote the wider use of the CPMS, therefore our results cannot be compared to already existing literature data  [8, 15].
During the course, attendees directly experienced the easiness of data sharing through the platform and might hand-touched the possibility of securely record patients' data and cases' discussions, the latter also instrumental for a teaching purpose. The training course was also an opportunity to identify criticalities for the CPMS usage as expressed by involved HCPs. These criticalities, seemingly trivial, may have a strong attrition effect on the widespread use of the platform. One of the prominent challenges emerged during the training was the somewhat complex operation of the many functions offered by the platform, a condition common to other ERNs  [8, 15]. However, in the initial approach to the platform, technical difficulties experienced by Italian HCPs were quite varied and included: (i) lack of clarity of the several steps.
It is worth mentioning that some ERN-provided services, CPMS included, are not covered by any financial instruments. Therefore, the CPMS becomes essentially a voluntary and "out of working hours" activity, requiring extra time allocated to its continuous use; this can easily translate in the under-usage of the CPMS platform, substantially hampering the long-term sustainability of the tool. Besides these aforementioned issues, the wider use of CPMS among Italian HCPs appears an opportunity with high potential but also as a resource to create evidence and reach oversight on progress made in Europe. Since within the platform clinical, genetic, diagnostic, and therapeutic data of patients can be securely stored, another expected result is the creation of a "map" of neuromuscular patients across Europe.
Furthermore, the joint experience of experts who follow relatively large numbers of neuromuscular patients would also create a platform to develop recommendations on diagnosis, monitoring, and treatments. Finally, the shared knowledge and experience makes CPMS a potential ideal environment to select patients for future research activities: sharing clinical data, researchers will be in a facilitated position for the creation of ERN Registries and Databases and the selection of patients for ERN research projects (e. g. clinical trials). This remarkable and probably underestimated capacity that CPMS has to act as patients' data repository should encourage further implementation, especially to maximize their interoperability with patients' registries.
Conclusions The CPMS is an excellent tool and opportunity to enhance the service given to patients suffering with RDs. Thanks to the ability of the platform to securely store clinical, genetic, diagnostic, and therapeutic data of patients, the EURO-NMD CPMS has the potential to become an electronic health record database of neuromuscular patients across Europe. Several approaches could be introduced or planned to promote the widespread use of the platform. Results presented in this paper clearly demonstrate that a tailored, ERN-oriented, training is a successful and promising strategy to significantly enhance the knowledge of CPMS and to implement its use in the daily practice. Similar courses should be designed through different ERNs in order to implement the use of CPMS and to benefit from strengths of e-health digital technologies.