dataesr/scientific-paragraphs-categorization huggingface dataset:
GRACE risk scores GRACE risk score models were calculated by adding the points assigned to the following eight variables recorded at hospital admission: age, heart rate, systolic blood pressure, serum creatinine, Killip class, cardiac arrest, STsegment deviation, and elevated cardiac enzymes/markers. Except for the Killip class, all variables were prospectively recorded in APACE and BACC. We approximated the Killip class for this analysis using the New York Heart Association (NYHA) functional class and the systolic blood pressure as outlined in Online Table  2. The binary variable elevated cardiac enzymes/markers was set to yes if the first determined hs-cTnT was > 14 ng/l. We calculated the following GRACE risk scores by means of R scripts (Online Appendix): • Original GRACE risk score estimating the risk for inhospital death, • GRACE 1.
Statistical analyses Missing data were handled by multiple imputation by chained equations (ten imputed data sets; R package mice). Multiple imputation was performed separately for the patients from APACE and BACC. All variables used for the multiple imputation are shown in Online Table  3  Sensitivity, specificity, and positive predictive value (PPV) together with their 95% confidence intervals (CI) were calculated to determine the diagnostic performances of the ESC 0/1 h-algorithm and GRACE risk scores (aim 2 and 3). The McNemar test was used to compare sensitivity and specificity and the test of Kosinski to compare the PPV between diagnostic tests  [12]. The impact on the diagnostic performance by combining the ESC 0/1 h-algorithm with a GRACE risk score to one test was also determined.
Results From April 2006 to April 2018, 8,766 patients were enrolled either in the APACE or the BACC studies. In total, 148 patients with the final diagnosis of STEMI or type 4 MI as well as missing information about the final diagnosis were excluded. This led to a final analysis population of 8,618 patients (Online Fig. 2 ). The proportion of missing data in variables necessary for the calculation of the GRACE risk score was low (Online Table  4 ). The variables with the most missing observations were the Killip class and ST-segment change (0. 8% each). Overall, 187 of 8,618 (2. 2%) patients had missing data on at least one variable. Only minor differences in baseline characteristics exist between patients without and with missing data (Online Table  5 ). Patients with missing data did not have a higher probability for the primary diagnostic endpoint during index hospitalization (15.
Limitations First, the Killip class was not prospectively assessed in both APACE and BACC. We, therefore, approximated the Killip class based on the NYHA class and systolic blood pressure on admission. This resulted in a similar distribution of Killip classes in comparison to the derivation cohort of the original GRACE risk score  [1]. Second, some variables necessary to calculate GRACE risk scores were missing. However, the proportion of missing values was low. Therefore, multiple imputation was used to handle missing data. By this, 2. 2% of patients could be retained in the analyses, which would have been excluded in a complete case analysis. Therefore, multiple imputation typically improves precision of estimates unless the mechanisms of missing is missing not at random which is not likely in our dataset  [19].
Funding Open Access funding enabled and organized by  Projekt DEAL. The  APACE  study was supported by research grants from the  Swiss National Science Foundation , the  Swiss Heart Foundation , the  KTI , the  European Union , the  Stiftung für kardiovaskuläre Forschung Basel , the  University of Basel ,  Abbott ,  Beckman Coulter ,  Biomerieux ,  Brahms ,  Roche ,  Siemens , and  Singulex. The BACC study was supported by  German Center of Cardiovascular Research (DZHK) , an unrestricted grant by  Abbott Diagnostics.
1b ). To avoid the oxidation in the SLM process, oxygen content in the working chamber was set to below 30 ppm via pumping a high-purity argon flow (99. 999 vol%). In this study, central composite face-centered design (CCF), a second order experimental design with a full factorial, was selected to code the independent variables and establish the experiment matrix via a Minitab Statistical Software. Ranges of the explored SLM parameters are listed in Table  2. Experiment matrix including independent variables (laser power, scanning velocity, and hatch distance) and dependent variables (average relative density and average microhardness) are summarized in Table  3  after a series of explorations. Then, response surface methodology (RSM) was conducted for optimizing the SLM parameters and a regression equation was calculated to predict the aforementioned responses.
Material characterization and mechanical properties An optical microscope (OM, Leica Dmi5000 m, Germany) and a field emission scanning electron microscope (FE-SEM, ZEISS GeminiSEM 300, Germany) equipped with an energy-dispersive x-ray spectroscopy (EDS, EDAX XLT TEM-SDD, America) were used to study the microstructure of the Mg alloy samples. Each specimen was carefully polished using SiC grinding papers and etched for 15 s in a mixed solvent of nitric acid and ethanol (4 ml nitric acid and 96 ml ethanol). The phase composition was detected by x-ray diffraction (XRD) using a RIGAKU Smartlab XRD equipped with 9 kW X-ray emitter at 40 kV and 100 mA in a 2θ range of 30-90 with a scanning step size of 0. 02 and scanning speed of 6 s per step. Crystallographic orientations were characterized via an electron back-scattering diffraction (EBSD) spectrometer (Oxford Ultim max 65, UK) and analyzed using the Channel 5 software.
Mechanical properties and tensile behavior Detailed mechanical properties of the Mg-9Al-1Zn-0. 5Mn samples fabricated via different process are presented in Fig. 10   [40] ), also shows an excellent prospect for bone implant applications. A UTS-At bubble map of the biomedical magnesium alloys fabricated using different processes is depicted in Fig. 11. Normally, UTS of the magnesium alloys are quite difficult to surpass 300 MPa as well as maintaining a good ductility (i. e. , A t >5%) if without a series of posttreatment processes. However, the ultra-low grain size and nanoparticle strengthening observed in Mg-9Al-1Zn-0. 5Mn SLM samples can enhance their mechanical properties. Thus, SLM-produced high-performance biomedical magnesium alloys offer sufficient data support and a theoretical basis for the customized medical implants.
Acknowledgements This work was supported by the  Sciences Platform Environment and Capacity Building Projects of GDAS  ( 2021GDASYL-20210102005 ), the  Guangdong Basic and Applied Basic Research Fund  ( 2020A1515111031 ,  2021A1515010939 ), the  Young Elite Scientist Sponsorship Program  by  China Association for Science and Technology (CAST)  ( YESS20210269 ),  Guangdong Provincial Special Support Program  ( 2019BT02C629 ),  Guangdong Academy of Sciences Development Special Fund Project  ( 2022GDASZH-2022010107 ),  Guangdong Academy of Sciences International Science and Technology Cooperation Platform Construction Project  ( 2022GDASZH-2022010203-003 ),  Guangzhou Key Field R&D Program  ( 20200702008 ). Fig. 13. (a) Strengthening mechanism-Contribution radar map of the SLM Mg-9Al-1Zn-0. 5Mnsample; (b) SLM-fabricated different types of the biomedical implants.
Acknowledgements G. F. acknowledges support from the  ANR  via the projects:  Indyana  under grant agreement  ANR-21-CE40-0008 ,  ChaMaNe  under grant agreement  ANR-19-CE40-0024  and an  ANITI  ( Artificial and Natural Intelligence Toulouse Institute ) Research Chair. R. V. is supported by  ANR OSCI-DEEP  ( ANR-19-NEUC-004 ) and an  ANITI (Artificial and Natural Intelligence Toulouse Institute) Research Chair  ( ANR-19-PI3A-0004 ).
The original studies were registered with ClinicalTrials. gov, NCT01468701, NCT01671007, and NCT01937377. Findings 34,421 patients were included (70. 0 ± 10. 5 years, 45. 1% females, 6900 (20. 0%) Asian: 3829 (55. 5%) Chinese, 814 (11. 8%) Japanese, 1964 (28. 5%) Korean and 293 (4. 2%) other Asian). Most of the Asian patients were recruited in Asia (n = 6701, 97. 1%), while non-Asian patients were mainly recruited in Europe (n = 15,449, 56. 1%) and North America (n = 8378, 30. 4%). Compared to non-Asian individuals, prescription of OAC and non-vitamin K antagonist oral anticoagulant (NOAC) was lower in Asian patients (Odds Ratio [OR] and 95% Confidence Intervals (CI): 0. 23 [0. 22-0. 25] and 0. 66 [0. 61-0. 71], respectively), but higher in the Japanese subgroup. Asian ethnicity was also associated with higher risk of OAC discontinuation (Hazard Ratio [HR] and [95% CI]: 1.
Definition of Asian ethnicity For this analysis, we included only those patients with available data regarding ethnicity (i. e. , being Asian or not). Ethnicity was self-reported by patients and collected in the electronic case report form. Specifically, patients were able to select "Asian" among the ethnicities proposed (which included also American Indian/ American Native/Alaska Native, Black/African American, White, Native Hawaiian/Other Pacific Islander, Arab/Middle East, African and Other), and if so, they were also able to sub-specify whether they were Chinese, Japanese, Korean, other Asian or South Asian. As these two last categories accounted for <1% of total patients, for purposes of this analysis, we grouped them together among "other Asian". For all the comparisons, the reference group was the one composed of non-Asians patients, which encompassed all other ethnicities (i.
Treatment, follow-up and major adverse outcomes At the baseline, data on antithrombotic prescription was collected by investigators. Details on follow-up and outcomes for GLORIA-AF Phase II and Phase III were reported elsewhere. 17, 18  Briefly, patients who received dabigatran during Phase II were followed-up for 2 years, while all patients enrolled during the Phase III of the program were followed-up, irrespective of the antithrombotic treatment received at baseline, for 3 years. During follow-up, data regarding OAC discontinuation and major outcomes were recorded, until study withdrawal, death or end of the study. In this analysis, we evaluated non-persistence and discontinuation only for patients who received OAC at baseline. We defined OAC discontinuation as either switching to another antithrombotic regimen (including switching to a different OAC), or interruption ≥30-days of the treatment received at baseline.
Results From the 36,617 patients originally enrolled in the GLORIA-AF Phase II and III Registry, 34,421 (94. 0%; mean age 70. 0 (10. 5) years, 15,518 (45. 1%) females) with available data on self-reported ethnicity were included in this analysis. Of these, 6900 (20%) were Asian: 3829 were Chinese (55. 5% of the total Asian group), 1964 Korean (28. 5%), 814 Japanese (11. 8%) and 293 other Asians (4. 2%). Among non-Asian patients, the vast majority (91. 3%) were White. Baseline characteristics according to ethnicity (Asian vs. non-Asian) and subgroups are reported in Supplementary Table  S1  and S2 in Supplementary Materials. Asian patients were younger, less likely female, showed lower BMI values, and had an overall lower burden of most comorbidities. Accordingly, baseline CHA 2 DS 2 -VASc and HAS-BLED scores were significantly lower among Asian patients.
Contributors GFR, BC, and GYHL conceived and designed the analysis, and verified the underlying data; GFR analysed data; GFR and BC interpreted data and drafted the manuscript; MP, DAM, JF, AB, GB, BO, YHC, MVH, TFC, and GYHL revised the manuscript and gave relevant intellectual contribution. GFR, BC, and GYHL had access to the dataset, all authors read and approved the final manuscript, and accept responsibility to submit for publication.
GYHL has been consultant and speaker for BMS/Pfizer, Boehringer Ingelheim, Anthos and Daiichi-Sankyo. No fees are directly received personally. All the disclosures happened outside the submitted work. GYHL is co-principal investigator of the AFFIRMO project on multimorbidity in AF, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 899871.
Acknowledgements This publication is based on research using data from data contributors  Boehringer Ingelheim  that has been made available through Vivli, Inc. Vivli has not contributed to or approved, and is not in any way responsible for, the contents of this publication. This study was funded by  Boehringer Ingelheim GmbH. The authors are solely responsible for the design and conduct of this study, all study analyses, the drafting and editing of the manuscript, and its final contents.
Data analysis The responses of all participants were recorded anonymously. Absolute frequencies and percentages were given for categorical data. Ordinal variables were reported with median and IQR, metric variables with mean and SD as well as median and IQR, both for the whole study population and subgroups (HPR in adult care (HPR-A) and HPR in paediatric care (HPR-P)). As a final step, we conducted subgroup analyses for the three most prominent professional groups (nurses, physiotherapists, occupational therapists) and the North, South, East and Central European regions. 7 e used natural language processing and the Latent Dirichlet Allocation (LDA)  8 9  to analyse the questionnaire's free text fields (feedback on current EULAR/ ESOR offerings). LDA is a technique used for unsupervised generative probabilistic topic modelling.
It aims to extract the meanings of a predefined number of topics. Each topic is characterised by high-frequency words and words that are best suited to distinguish it from other topics. To perform LDA, we created a semantic space by downloading and translating all the answers into English. Then, we took the following steps: stemming the words, removing stop words, casting the text into lowercase only and removing punctuations, names and personal references. We then explored the frequency of words and correlations between the co-occurrence of words and extracted five topics based on LDA. The number of topics was determined by examining the heatmaps and the coherence of the words in the topics. Heatmaps are a popular graphical method for visualising highdimensional data. In a heatmap, tables of data are coded as coloured cells.
Regression analysis We used multiple logistic regression models to determine higher postgraduate educational readiness factors. We operationalised educational readiness by means of four variables: first, in terms of knowledge (Have you ever heard of EULAR/ESOR?) and second, in terms of action taken (Have you ever attended the annual EULAR Congress/ESOR offerings?). The independent variables were selected from participants' personal data (age, gender, occupation, work with children/adults, the highest level of education completed, work experience with people with RMD and time spent working in clinical patient care/research/organisational roles). All variables were tested for multicollinearity prior to the regression analysis. 10 11 All analyses were conducted using R (http:// www. r-project.
Acknowledgements We would like to thank  Siniša Grabovac  for translating the questionnaire into Croatian and Serbian. Contributors VR, EM, MRA, A-KRO, JC, TPMVV, TS built the first draft of the questionnaire. All authors reviewed the questionnaire and translated it into their respective languages. All authors distributed the questionnaire within their country and networks. VR, LS, EM and TS analysed and interpreted the data. All authors suggested and agreed upon the research questions. VR, LS, EM and TS wrote the first draft of the manuscript. All authors read and gave feedback to the manuscript and made contributions to the text. All authors approved the final version of the manuscript. TS and VR are the guarantor of the study. They accept full responsibility for the work and the conduct of the study, had access to the data, and controlled the decision to publish.
ACKNOWLEDGMENTS This study was supported by  Provincia Autonoma of Trento  through "NeuSurPlan: an integrated approach to neurosurgery planning based on multimodal data. "funding granted to  Azienda Provinciale per i Servizi Sanitari  ( ID 2021-D337-00094 ). The Authors are grateful to the  Health Department of the Provincia Autonoma of Trento  and to the  Direction Team of the Azienda Provinciale per i Servizi Sanitari  for their support to NeuSurPlan Project.
Methods This study used data from the prospective EPIC cohort study, which recruited participants between March 18, 1991, and July 2, 2001, from 23 centres in ten European countries. Participant eligibility within each cohort was based on geographical or administrative boundaries. Participants were excluded if they had a cancer diagnosis before recruitment, had missing information for the NOVA food processing classification, or were within the top and bottom 1% for ratio of energy intake to energy requirement. Validated dietary questionnaires were used to obtain information on food and drink consumption. Participants with cancer were identified using cancer registries or during follow-up from a combination of sources, including cancer and pathology centres, health insurance records, and active follow-up of participants.
Research in context Evidence before this study We searched Medline, Web of Science, and Google Scholar with the search terms "food processing", "ultra-process*", "NOVA", and "cancer", for publications published in English from database inception until June, 2022. We found that epidemiological evidence has suggested a positive association between consumption of ultra-processed food and breast cancer, colorectal cancer, and chronic lymphocytic leukaemia outcomes. However, some conflicting results have also been reported. The evidence regarding associations between dietary intakes of minimally processed food, as assessed by the NOVA classification, and cancer risk is scarce, with only a few studies reporting a positive association between consumption of processed food and prostate cancer risk and an inverse association between consumption of minimally processed food and breast cancer risk.
Procedures At baseline (ie, recruitment into the EPIC cohort) questionnaires were used to obtain information on gender, physical activity, education, smoking, alcohol consumption, and reproductive factors, as described elsewhere. 21 Gender data were collected via self-report questionnaires and options were male or female. Bodyweight and height were measured in all centres, except for Oxford (UK), France, and Norway where these data were self-reported. However, these self-reported anthropometric measures were shown to be valid for identifying associations in epidemiological studies. 23, 24 Assessed weight and height measurements were used to calculate BMI. Validated country-specific or centre-specific dietary questionnaires were used to obtain information on food consumption. In most centres, dietary questionnaires were self-administered, except for Ragusa (Italy), Naples (Italy), and Spain, where face-to-face interviews were performed by trained personnel.
More details on the classification of EPIC foods into the NOVA classification system and the different scenarios are included in a published descriptive paper. 25  Data are mean (SD) or n (%). All differences in baseline characteristics between quartiles were significant (all p<0•001). Quartile 1 contains participants with the lowest consumption of ultra-processed foods, and quartile 4 contains those with the highest consumption of ultra-processed foods. NOVA 1=unprocessed or minimally processed foods. NOVA 2=processed culinary ingredients. NOVA 3=processed foods. NOVA 4=ultra-processed foods.
Statistical analysis The main analyses were performed using the middlebound scenario for the NOVA classification. The daily percentage intake in grams was used because it also considers foods that do not provide energy (eg, artificially sweetened drinks) and non-nutritional factors associated with food processing (eg, neoformed contaminants). Baseline characteristics were examined for the total population and by sex-specific quartiles for the daily percentage intake in grams of each NOVA food group. Descriptive analyses were performed for each NOVA food group considering the absolute daily intake in calories and grams and the percentage intake. Individuals with missing data for the NOVA category were not included. The associations between the percentage intake of each NOVA group in grams and the incidence of cancers were assessed using Cox proportional hazards regression models.
Results Between March 18, 1991, and July 2, 2001, 521 324 participants were recruited and 450 111 were included in this analysis. 71 213 participants were excluded because they had a cancer diagnosis before recruitment, had missing information for the NOVA classification, or were within the extreme ranking (top and bottom 1%) of the ratio of energy intake to energy requirement. Participants from Greece had to be excluded due to data access issues for this country. 318 686 (70•8%) of 450 111 participants were female individuals and 131 425 (29•2%) were male individuals, and 47 573 participants were diagnosed with cancer, with a mean of 14 A NOVA 1 N OVA 2 N OVA 3 N OVA 4 1•0 0•5 0•3 0•2 HR (95% CI) Quartile Quartile 1 Quartile 2 Q uartile 3 Q uartile 4 2•0 2•0 1•0 0•5 0•3 HR (95% CI) HR (95% CI) HR (95% CI) 0•7 5•0 3•0 1•0 0•5 2•0 5•0 1•0 0•7 0•5 2•0 (Figure continues on next page) higher education, and more likely to be physically active, had a higher intake of energy, sodium, fat, and carbohydrate and a lower intake of alcohol, and had a lower score for the Mediterranean diet than participants in the lowest quartile of ultra-processed food consumption (appendix pp 3-4).
Acknowledgments This work was supported by  Cancer Research UK  ( C33493/A29678 ),  World Cancer Research Fund International  ( IIG_FULL_2020_033 ), and the  Institut National du Cancer  (INCa number  2021-138 ). The coordination of EPIC is financially supported by the  IARC  and the  Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London , which has additional infrastructure support provided by the  UK National Institute for Health and Care Research Imperial Biomedical Research Centre. The national cohorts are supported by the  Danish Cancer Society (Denmark) ;  Ligue Contre le Cancer ,  Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale ,  Institut National de la Santé et de la Recherche Médicale (INSERM; France) ;  German Cancer Aid ,  German Cancer Research Center (DKFZ) ,  German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE) ,  Federal Ministry of Education and Research (BMBF; Germany) ;  Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, Compagnia di SanPaolo and National Research Council (Italy) ;  Dutch Ministry of Public Health, Welfare and Sports (VWS) ,  Netherlands Cancer Registry (NKR) ,  LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland) ,  World Cancer Research Fund ,  Statistics Netherlands  (Netherlands);  Health Research Fund (FIS)-Instituto de Salud Carlos III (ISCIII) ,  Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra , and the  Catalan Institute of Oncology (ICO; Spain) ;  Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden) ; and  Cancer Research UK  ( 14136  to  EPIC-Norfolk ;  C8221/A29017  to  EPIC-Oxford ) and  Medical Research Council  ( 1000143  to  EPIC-Norfolk ;  MR/M012190/1  to EPIC-Oxford; UK).