dataesr/scientific-paragraphs-categorization huggingface dataset:
The scintillator and water detectors are equipped with PMTs, and act as neutron and muon veto, respectively. More details on the detector can be found in Refs. [5, [35] [36] [37] [38]. We perform this analysis using the 653. 1 live-days DS-50 dataset, from December 12, 2015 to October 4, 2017. We use the ionization signal S2 since it has significantly lower detection threshold than S1 thanks to the gain of the electroluminescence. The region of interest (ROI) is defined as where the ionization response is calibrated  [39]  and backgrounds are well understood. We characterize the strength of the ionization signal by the number of electrons that are extracted into the gas-region at the top of the TPC. Given the electric field settings of the TPC, the extraction efficiency for electrons from the liquid into the gas is essentially 100% and so N e is a good measure of the ionization signal.
[40]. An additional set of selection cuts is applied to remove spurious S2 pulses mainly induced by electrons captured by impurities, events with an echo from surface alphas that lose normal S2 to the TPC wall, and pile up events, associated to random coincidences between very low S1 and S2 pulses from the anode. The final dataset accounting for the quality and selection cuts corresponds to an exposure of ð12 306 AE 184Þ kg d. The overall acceptance, almost flat with respect to the recoil energy, varies from 38% at 4e -to 40% at higher than 15e -. The major sources of background events in the ROI and in the fiducial volume are 39 Ar and 85 Kr decays occurring in the LAr bulk, whose rates are expected to be ð6. 5 AE 0. 9Þ × 10 -4 and ð1. 7 AE 0. 1Þ × 10 -3 Hz, respectively, and γ rays and x rays from radioactive contaminants in the PMTs and stainless-steel cryostat, which contribute at ð3.
The ionization response to electronic recoils has been measured down to 180 eV er and a fit to the data with a function of the Thomas-Imel box model form allows an extrapolation down to Oð100 eVÞ. Similarly, the expected number of ionization electrons for NR is given by the Thomas-Imel box model, where the number of electron-ion pairs is obtained with Bezrukov's model  [42]  and with the Ziegler et al. model for the nuclear screening function  [43]. The ionization response to NR has been measured down to 500 eV nr. This is the lowest threshold ever reached in liquid argon and corresponds to 3 ionization electrons. The ER and NR ionization models are constrained by fitting the 241 Am 9 Be and 241 Am 13 C neutron sources data, β-decay data of 39 Ar, and electron captures of 37 Ar obtained during the DS-50 calibration campaign, and by external datasets from the SCENE  [44] , ARIS  [45] , and Joshi et al.
6811/IA/SP/2018 ). We also acknowledge financial support from the  French Institut National de Physique Nucléaire et de Physique des Particules  ( IN2P3 ), the  IN2P3-COPIN consortium  (Grant No. 20-152 ), and the  UnivEarthS LabEx program  (Grants No. ANR-10-LABX-0023  and No. ANR-18-IDEX-0001 ), from the  São Paulo Research Foundation (FAPESP)  (Grant No. 2016/09084-0 ), from the  Interdisciplinary Scientific and Educational School of Moscow University "Fundamental and Applied Space Research," from the Program of the Ministry of Education and Science of the Russian Federation  for higher education establishments, project No. FZWG-2020-0032  ( 2019-1569 ), from  IRAP AstroCeNT  funded by  FNP  from  ERDF , and from the  Science and Technology Facilities Council ,  United Kingdom. I. Albuquerque  is partially supported by the  Brazilian Research Council (CNPq).
Acknowledgments M. Miniaci  is supported by the  EU H2020 ERC StG «POSEIDON» , Grant Agreement No. 101039576. A. Bergamini ,  F. Bosia ,  N. M. Pugno ,  M. Miniaci  are supported by the  EU H2020 FET Open «BOHEME» , Grant Agreement No. 863179. C. Daraio  acknowledges financial support from the  Department of Energy  under grant  DE-SC0021253.
Background Chronic obstructive pulmonary disease (COPD) is the most common indication for long-term domiciliary non-invasive ventilation (NIV) but there is uncertainty in data supporting current guidelines. This study described health trajectories before initiation of at-home NIV in people with COPD, and compared mortality outcomes between groups with different pre-NIV health trajectories. Methods Data were from the French national health insurance reimbursement system database for individuals with COPD aged ≥40 years and ≥1 reimbursement for NIV between 1 January 2015 and 31 December 2019. Common health trajectories were determined using time sequence analysis through K-clustering (TAK analysis). Findings Data from 54,545 individuals were analysed; the population was elderly (median age 70 years) with multiple comorbidities.
Introduction Chronic obstructive pulmonary disease (COPD) is a common respiratory condition that is characterised by gradual worsening of symptoms and lung function. Worldwide in 2019 there were 202. 3 million prevalent cases of COPD, 3. 3 million COPD-related deaths, and COPD was responsible for the loss of 74. 4 million disability-adjusted life-years. 1  The global age-standardised prevalence of COPD is 2638 per 100,000 population. 1  Although these statistics alone are sobering, available The Lancet Regional Health -Europe 2023;34: 100717 data suggest that underdiagnosis is also a significant issue, meaning that a large number of individuals who have undiagnosed COPD may not be appropriately treated. 2  Overall, COPD is now a global health crisis. 3  Acute exacerbations are a key feature of COPD, and these become increasingly common as the disease progresses.
This highlights the need to identify different health trajectories before the initiation of at-home NIV. This analysis used data from the French national health insurance reimbursement system database ("Système national des données de santé"; SNDS)a world-class database that covers 99% of the French health system-to describe health trajectories before the initiation of at-home NIV in people with COPD, and determine whether different health trajectories are associated with different mortality outcomes. The hypothesis was that specific health trajectories before NIV initiation would be representative of different COPD phenotypes/clinical subtypes, prescriber behaviours, and health care system organisation.
Added value of this study The French national health insurance reimbursement system database used in this study is a large unbiased, well anonymised claims database that covers >99% of the French population. This means that study data are robust and reflect population-wide practices. These data address a current gap in the literature because there is also a lack of real-world evidence regarding health trajectories prior to the initiation of at-home NIV in individuals with COPD. The identification of four distinct groups showing quite different pre-NIV health trajectories, and the finding of different mortality rates in these groups, could be used to reduce health inequities and facilitate a more personalised and evidence-based approach to the use of NIV in stable COPD. Implications of all the available evidence Prescription of at-home NIV in individuals with COPD occurs at a variety of different stages in the disease trajectory.
The different approaches used in the four clusters identified in this analysis likely reflect the current lack of consistent evidence and consensus with respect to guideline recommendations for NIV set-up and initiation. population living in France and collects individual pseudonymised data from beneficiaries of almost all health insurance schemes. Data availability varies by year and scheme. Therefore, for consistency throughout the study period, we restricted data extraction to the general scheme (for private sector employees), covering approximately 76% of the French population (around 40 million adults). Retirees, unemployed people, and welfare beneficiaries are covered by the scheme corresponding to their former occupation. The SNDS contains socio-demographic data (age, sex, and area), and information on all health care expenses, including outpatient visits, reimbursed medication, medical procedures, hospital admission diagnoses and procedures, and date of death.
Although diagnoses and purpose of outpatient visits, and the results of laboratory tests or other examinations, are not available, the SNDS contains a list of 30 long-term chronic diseases coded according to the International Classification of Diseases 10th revision (ICD-10), for which patients are granted full reimbursement for disease-related healthcare expenses. Specific approvals for this study were obtained from the Comité éthique et scientifique pour les recherches, les études et les évaluations dans le domaine de la santé (CESREES; ref: 3904033) and from the "Commission Nationale Informatique et Liberté" (CNIL), the French information technology and personal data protection authority (DR 2021 162 and n  • 921,198).
Outcomes definition Only severe exacerbations of COPD that required hospitalisation were recorded. These were identified using the published algorithm validated by the French Institute for Public Health Surveillance (InVS). InVS serves as reference governance body for methodology of studies based on the French health insurance database in France. The algorithm included the different ICD-10 diagnoses and their prioritised score as principal diagnosis, related diagnosis and/or associated diagnosis to have an accurate characterization of severe COPD exacerbations (see Supplementary Methods in the online supplement for full details). This algorithm has been used in previous studies that utilised SNDS data. 15, 16  Hospitalisations were split into two groups: hospitalisations in respiratory ward, cardiology or intensive care units, and other types of hospitalisations.
Discussion These data address a current lack of evidence relating to health trajectories prior to the start of home NIV using a unique dataset that included all patients started on longterm NIV in France between 2015 and 2019. There were four distinct health trajectories before the initiation of NIV. Factors that differed between the four health trajectory clusters included patient factors such as age, sex and comorbidities, healthcare factors such as investigations prior to NIV, and the prescriber and location of NIV initiation. The lack of both consensus and strong evidence to support current recommendations for NIV set-up and initiation  [10] [11] [12]  and the multiple different COPD phenotypes could explain the variety of different health trajectories seen before the start of NIV therapy in this group of French individuals with COPD.
Articles A notable feature of Cluster 3 was that use of supplemental oxygen therapy was common. This was not due to older age because age did not differ from that in other clusters, and palliative care was excluded. Therefore, we hypothesise that this reflects the characteristic of this cluster as having been started on NIV after several AECOPD, increasing the likelihood that oxygen therapy was prescribed before consideration of NIV. Cluster 4 represents the sickest population, who had delayed access to care and were probably less good at utilising healthcare systems. Our dataset did not include information on ethnicity or poverty, but we can speculate that geographic and socioeconomic differences were present. Even in Europe, COPD is a disease of poverty. Differences in coverage between insurers disproportionally impact non-White and low socioeconomic status individuals with COPD.
Certainly, the significantly higher mortality in Cluster 4, for whom initiation of NIV was quite delayed, suggest that earlier identification of individuals suitable for home NIV and prompt initiation of therapy might be beneficial. Similar to other recent studies,  28, 29  a key strength of the current analysis is the dataset used. The French SNDS is a large unbiased claims database that covers 99% of the French population. It includes public and private practices, and in-hospital and ambulatory NIV initiation, and is not specific to any healthcare provider, insurer or device. In addition, the sample size is by far the largest compared with any previous study of NIV in COPD. Another important point is that the entire study period took place before the start of the COVID-19 pandemic, meaning that there is no confusion between NIV initiated for COPD and NIV required to manage COVID-19-related illness.
Several limitations also need to be taken into account when interpreting the study findings. Firstly, the database was designed for administration rather than research purposes and may therefore not contain information on some relevant parameters. For example, due to French privacy requirements, the SNDS does not contain data on smoking habits, alcohol intake or body mass index. In addition, it is important to note that stringent methodology was applied to specifically include only severe AECOPD that required hospitalisation in the current analysis. This is because severe AECOPD requiring hospitalisation can be determined objectively, whereas it is more difficult to identify exacerbations not requiring hospitalisation with consistency and acceptable reliability. In that context, previous data suggest that moderate AECOPD may not be accurately identified by healthcare utilization-based algorithms.
Acknowledgements Medical writing assistance was provided by  Nicola Ryan , independent medical writer, funded by  ResMed. Data management and analyses were conducted by  Sacha Hiridjee  and  Eleonore Herquelot  with supervision from  Aurélie Schmidt , employees of Heva. The authors would also like to thank the  Direction de la Stratégie, des Études   et des Statistiques (DSES) ,  Département Accès ,  Traitement et Analyse de la Donnée (DATAD) , and  Cellule de la CNAM en Charge de l'accompagnement des Demandes D'extraction (DEMEX)  teams at the  Caisse Nationale de l'Assurance maladie (CNAM)  for the data extraction.
Funding JLP and SB are supported by the  French National Research Agency  in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ) and the " e-health and integrated care and trajectories medicine and MIAI artificial intelligence  ( ANR-19-P3IA-0003 )" Chairs of excellence from the  Grenoble Alpes University Foundation. This work was supported by  ResMed.
Standardized vs. Non-standardized Data When a physician, for example Mr. Russo's cardiologist, documents the medical treatment, the data used to describe the treatment, for example in the discharge letter, may be recorded as continuous narrative text without further structuring. We refer to such free text as non-standardized data. With free text, a situation can be described in detail and exactly using full linguistic expressiveness-if there is enough time. The disadvantage of such non-standardized data is, however, that the recorded data are hardly comparable, their completeness cannot be checked, and further processing, especially interpreting its semantics by a machine, is very difficult. However, there are promising approaches from artificial intelligence to use natural language processing (NLP) to tag free texts with terminologies (e.
Entity type Description Diagnosis Diagnoses are the identified cause or nature of patients' diseases or medical conditions Discharge summary Discharge summaries briefly summarize diagnoses, treatment, and recommendations from the discharging health care facility. The discharge summary is necessary for the receiving health care facilities in order to be able to provide further treatment Finding Findings summarize the results of diagnostic procedures for patients such as lab and X-ray examinations. Laboratory findings may consist of the measured values of clinical chemical parameters. But they may also contain complex genetic data from sequencing, also called "omics" data. Each type of data requires specialized software to present these data to medical personnel in such a way that they can interpret them well for diagnostic purposes Health record Health records are descriptions about a person's past and present health conditions, for example, disease history, systems review, social history, past medical history, family history, or medication.
Medical history Medical histories comprise all information collected for a patient which is needed as a basis for medical care planning. The documented medical history is the result of medical history taking between the health care professional and the patient Medical procedure Medical procedures are procedures performed by physicians for patients, for example, X-ray examinations or operations Nursing history Nursing histories comprise all information collected for a patient which is needed as a basis for nursing care planning. The documented nursing history is the result of the admission interview between the nurse and the patient Nursing procedure Nursing procedures are procedures performed by nurses for patients, for example, taking blood or taking the temperature 3. 2 Domain Layer: Data to be Processed and Provided.
Entity Types About Knowledge It is not only information about patients that health care professionals need for care. Medical and nursing knowledge is also required. Medical and nursing knowledge comprises knowledge of various medical and nursing specialties, for example, knowledge about diseases, symptoms, side effects, treatments, and health risks. Knowledge is stored in physicians' and nurses' heads as well as in various media such as books, journals, guidelines, webpages, or databases. A very important source of medical and nursing knowledge is MEDLINE, a bibliographic database of medical publications. It takes a lot of effort to select and acquire the knowledge that a person needs for a specific task from the amount of available knowledge. We therefore want to distinguish available and selected knowledge.
Patient Admission The prerequisite for the treatment of a sick person by a health care professional in a health care facility is the admission of the person to that facility. Patient admission (short: admission) aims at recording and distributing the patient demographic and insurance data as well as data on the medical and nursing history (Sect. 3. 2. 3. 2). In addition, each patient must be identified correctly and a unique patient and case identification number (CIN) must be assigned. This function can be decomposed as follows (Fig. 2. 4 in Sect. 2. 14. 1):.
Administrative Admission Administrative admission starts following patient identification. It creates the socalled case, being the aggregation of several contacts clustered according to specific clinical and/or organizational purposes of the facility. In case of inpatient treatment, a case summarizes the stay at the facility from patient admission until discharge. Each case is uniquely identified by its CIN. Important administrative data, such as insurance data or details about booked services, patient's relatives, admitting physician, and transfer diagnoses, must be recorded. The patient is assigned to a certain area, for example, a ward and a bed. Some of the administrative data must be available to other functions through the help of certain organizational media (such as adhesive labels and smart cards).
Medical Admission At the first contact with the patient, the physician will carry out the medical admission. This typically comprises recording the patients' medical history (disease history, systems review, social history, past medical history, family history, medication). Some of this information may be collected from documents of the referring physician which are taken to the facility by the patients themselves. As a result of medical admission, the admission diagnosis must be stated and coded using the mandatory classification, for example, ICD-11. The medical history must be made available for other functions by including it with the patient's health record. However, some facilities only have access to patient-related documents and data that originated from the same facility, i. e. , the facility's patient record.
Execution of Diagnostic, Therapeutic, and Nursing Procedures The planned diagnostic, therapeutic, or nursing procedures (such as operations, radiotherapy, radiological examinations, medication) must be executed. Adequate tools and resources (e. g. , staff, room, equipment) for the execution of the necessary procedures have to be available. This must be managed according to the special needs of the respective facility and health care setting (Chap. 6). It is important that changes in care planning that may be due to new findings are promptly communicated to all involved units, facilities, and persons, enabling them to adapt to the new situation. All clinically relevant patient data (such as vital signs, orders, results, decisions) must be recorded as completely, correctly, and quickly as necessary.
This supports the coordination of patient treatment among all persons and facilities involved and provides the legal justification for the actions taken. In inpatient care, a lot of these data may be recorded, for example, on the ward. In outpatient care, however, with the health care professionals caring for the patient at home, much of the data must also be collected and documented at the patient's home. As far as possible and whenever sensible, data and metadata should be recorded and represented in a standardized manner (compare 3. 2. 2) to allow for seamless care across providers, for data aggregation and statistics, for computerized decision support, and for data retrieval. It is important that data can be linked by PIN and CIN even when data originate from different areas (such as ward, service unit, outpatient unit, home).
Application software products for PACS also comprise means for image processing and 3D reconstruction. They are often offered by vendors, which also offer physical data processing systems such as storage, networks, and modalities. Alternatively, vendor-neutral archives (VNAs, Sect. 3. 9. 3) can be used to store image data and other patient-related documents. VNAs are connected to the RIS, the image processing components of the PACS, and other application systems using communication standards (especially DICOM). The use of the communication standards here follows the rules provided in the Integrating the Health care Enterprise (IHE) profiles for sharing documents (Sects. 3. 7. 2. 4 and 3. 7. 2. 6). Obviously, RIS and PACS in one health care facility should be closely connected. They should also have a tight connection to the facility's patient administration system, MDMS, CPOE system, and the patient data management system (PDMS) in order to allow quick access to reports and imaging pictures from every unit.
Loading data, for example from the MDMS, LIS, RIS, or CPOE system into a DWS, the recruitment of patients for clinical trials, and the statistical evaluation of patient data can be effectively supported. Data warehouse systems can help to answer the aforementioned questions by pooling management-relevant data from other systems such as the ERPS and the CPOE system and providing means to analyze these data through data mining techniques. A DWS supporting management of health care facilities is often called a business intelligence system. Extracted data in the DWS have been transferred and aggregated into a suitable format and then actively loaded into the data warehouse (push principle). A specific request on the data will only access data already loaded into the data warehouse. An ISO standard exists which provides implementation guidance for data warehouses in health information systems  [6].