dataesr/scientific-paragraphs-categorization huggingface dataset:
We introduced fiducial boundaries that encompass a well-behaved and predictable acceptance region in azimuthal angle, φ, of the detector for each particle depending upon its momentum, charge, and polar angle, θ. Hence, a standard geometric fiducial cut procedure  [15]  was applied for each detected particle in all six sectors, both for the data and for the simulation. Scintillation counters were used to determine the time of flight of charged particles. Counter with very low photomultiplier gain resulted in poor timing resolution and poor efficiency. These bad paddles were identified and removed from the data analysis during the event selection process  [15].
The differential cross sections can be modeled as a function of t-slope by, dσ dt = σ 0 e -bt , (5) where dσ dt is the differential cross section, b is the t-slope parameter, and σ 0 is the amplitude of the cross section. In order to best estimate the b-value for the simulation so that it matched our data, different values of b were used to generate different sets of Monte-Carlo simulations, which were compared with the data distributions versus t. As a result, detector acceptance (or efficiency) was calculated with simulated events using b = 1. 5 GeV -2 and b = 2. 0 GeV -2 for W ≤ 2. 85 GeV and W > 2. 85 GeV, respectively. There were multiple triggers set up during the g12 experiment. The trigger relevant to our reaction is the one where events were recorded with three charged particles detected in three different sectors of CLAS.
Acknowledgments I thank the two anonymous reviewers,  Olaf Corry ,  Adrien Estève ,  Lucile Maertens , and  Julia Maria Trombetta  for their constructive criticism. This research partly draws on the project  Climate Change and Security in the United Nations Security Council  ( 2019-2020 ) and was funded by the  German Federal Ministry of Education and Research  in the context of the research project Multiple Crises: Covid-19 and the  Entanglements of Public Health, Security and Ecology in Europe  ( 2021-2024 ) associated with the CMB.
Methods : Data were included from placebo patients enrolled in ASCEND (NCT01366209) and CAPACITY (NCT00287716, NCT00287729), and all patients in INSPIRE (NCT00075998) and RIFF Cohort A (NCT01872689). An additional analysis included data from pirfenidonetreated patients. Outcomes (annualized change in percent predicted forced vital capacity [%FVC], percent predicted carbon monoxide diffusing capacity, 6-min walk distance, St. George's Respiratory Questionnaire total score, hospitalization, mortality, and serious adverse events) were analyzed by baseline BMI (<25 kg/m 2 , 25 kg/m 2 -<30 kg/m 2 , or ≥30 kg/m 2 ) and annualized percent change in body weight (no loss, >0-<5% loss, or ≥5% loss). Results: Placebo-treated patients with baseline BMI <25 kg/m 2 or annualized weight loss may experience worse outcomes, versus those with baseline BMI ≥25 kg/m 2 or no weight loss.
INSPIRE [NCT00075998], and RIFF [NCT01872689] ). The designs of these trials have been published previously. Briefly, ASCEND and CAPACITY were Phase III, double-blind trials of pirfenidone versus placebo  [18, 19] ; INSPIRE was a Phase III, placebo-controlled trial that evaluated the efficacy of interferon (IFN)-γ-1b  [20] ; and RIFF (Cohort A) assessed lebrikizumab monotherapy versus placebo in a Phase II study  [21]. The analysis presented here included patients from the placebo arms of ASCEND and CAPACITY, and all patients from INSPIRE and RIFF (Cohort A). All patients from the INSPIRE and RIFF (Cohort A) trials were included as no treatment effect was detected in the active treatment arms of these trials  [20, 21]. The lack of treatment difference observed during these trials enabled the inclusion of all patients to provide a larger pooled population than would be obtained by including patients in the placebo group only.
Statistical Analyses Baseline demographics and characteristics, stratified by baseline BMI category, were reported descriptively. For each patient with a baseline and at least one post-baseline measurement of body weight, an annualized percent change in body weight was determined from a linear mixed model for repeated measures of body weight versus actual time since baseline weight assessment (Day 1). The model included both random intercept and random slope terms (assuming different change pattern among patients). For each patient, the predicted 1-year weight change was estimated and percent change calculated using the baseline weight. Patients who discontinued or died prior to 1 year were included in the analyses if weight data were available. Any patient with missing baseline weight or no post-baseline measurement was excluded.
All available body weights for each patient were included in the repeated-measures model. The proportions of patients with an absolute or relative decline of ≥10% in percent predicted FVC or death up to 1 year post-randomization, stratified by baseline BMI category and annualized percent change in body weight, were reported descriptively. Clinical outcomes up to 1 year were presented as estimates (with 95% confidence intervals  [CIs] ) and estimated differences (with 95% CIs), from the reference category. For assessment according to BMI, the reference category was baseline BMI <25 kg/m 2 , and for assessment according to annualized percent change in body weight, the reference category was no weight loss. For continuous data (changes in percent predicted FVC, percent predicted DLco, 6MWD, and SGRQ total score), estimates were based on repeatedmeasures analysis of covariance, with study, age, sex, race, baseline high-resolution computed tomography (HRCT) status, years since IPF diagnosis, baseline oxygen use, baseline smoking status, the categorical variable of interest (baseline BMI category or annualized percent change in body-weight category), time, and annualized categorical variable of interest*time as fixed-effect covariates, with random intercept and random slope for time for each patient.
For these outcomes, estimated annual rates of change were obtained for patients who dropped out or died prior to 1 year if the baseline assessment and at least one post-baseline assessment were available. For binary data (any hospitalization, mortality, absolute or relative decline of ≥10% in percent predicted FVC or death, and treatment-emergent SAEs), estimates were based on logistic regression with study, age, sex, race, baseline HRCT status, years since IPF diagnosis, baseline oxygen use, baseline smoking status, and the categorical variable of interest as model factors. For these outcomes, patients who dropped out or died prior to 1 year were categorized based on available data to the point of discontinuation or death. Time-to-event analyses of first all-cause hospitalization and mortality are also presented by stratified annualized percent change in body weight.
Future studies in IPF should incorporate validated methods to assess body composition and muscle function to better understand the relationship between weight loss/BMI and poor outcomes. Low baseline BMI has been suggested as a predictor of poor antifibrotic tolerance  [37] ; however, this was not assessed in our post hoc analysis. Therefore, it may be of value for future studies to also investigate BMI-adjusted doses of antifibrotics in patients with IPF. Furthermore, it may also be important for future studies to consider the association between pleuroparenchymal fibroelastosis (PPFE), which can often be misdiagnosed as IPF or other forms of ILD, and weight loss  [38]. Limitations of this analysis include that this was a post hoc analysis from studies that were not designed to assess the effects of BMI or weight loss on outcomes, nor was the type I error controlled for positive findings-prospective datasets are required to evaluate whether weight loss is independently associated with clinical outcomes.
As this study only looked at outcomes over 1 year, further research is required to assess the impact of weight loss on outcomes over a longer time period. A further limitation of this analysis is that because annualized weight loss was measured over the same 1-year period as the outcomes investigated and took into account all data available during this time period, it is not possible to determine if IPF is causative of weight loss and/or if other factors, such as SAEs, hospitalization, or declining functional status, need to be considered. Therefore, although factors associated with weight loss in IPF have been identified previously  [39] , further studies that assess weight loss prior to the outcome event are required. Additionally, because BMI and annualized weight loss were not analyzed as continuous variables, it is not possible to comment on whether a linear relationship exists between these predictors and the outcomes in this analysis.
Background: We described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019. Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza. Methods: We conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes.
Study design and setting This multinational cohort study was part of the CHARYBDIS (Characterizing Health Associated Risks, and Your Baseline Disease In SARS-COV-2) project, designed by the Observational Health Data Sciences and Informatics (OHDSI) community. CHARYBDIS is a large-scale study aiming to characterize individuals with COVID-19 using routinely-collected healthcare data (protocol available at https:// www. ohdsi. org/wp-content/uploads/2020/07/Protocol_COVID-19-Charybdis-Characterisation_V5. docx). Twenty-two databases standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM; ref. 10 ) have contributed to CHARYBDIS to date. The OHDSI network maintains the OMOP-CDM, and its members have developed a wide range of tools to facilitate analyses of such mapped data  (11).
Results for this substudy were extracted from the overarching result set on January 29, 2021. We included those databases reporting on at least 140 subjects with a history of cancer diagnosed and/or hospitalized with COVID-19. This cut-off was established to estimate the prevalence of conditions affecting 10% of the study population with a confidence interval (CI) width of AE5%. The selection process of databases is depicted in Supplementary Fig. S1. Eight databases from Spain and the United States were included in this study. Spanish data came from the Information System for Research in Primary Care (SIDIAP) database, a primary care database from Catalonia, a northeastern region in Spain  (12)   S1. SIDIAP and CUIMC included patients with COVID-19 identified from March to May 2020, HealthVerity, and STARR-OMOP spanned to June 2020, CU-AMC-HDC to July 2020, VA-OMOP to September 2020, and IQVIA-OpenClaims and Optum-EHR to October 2020.
We report here sex, age, race, antineoplastic and immunomodulating treatment received the month and year prior to index date, and key comorbidities. The only information available for race was the proportion of African American patients, which was reported in four databases (CU-AMC-HDC, CUIMC, Optum-EHR, and VA-OMOP). The 30-day outcomes of interest in the diagnosed cohort were hospitalization and death (from all causes). In the hospitalized cohorts (COVID-19 and influenza), the outcomes of interest were acute respiratory distress syndrome (ARDS), acute kidney injury (AKI), cardiovascular disease events, deep vein thrombosis, pulmonary embolism, sepsis, requirement of intensive services (identified by a recorded mechanical ventilation and/or a tracheostomy and/or extracorporeal membrane oxygenation procedure), and death (from all causes).
Analysis Analysis was performed through a federated analysis approach  (15). Following a prespecified analysis plan, an analytical code for the whole CHARYBDIS study was developed and run locally in each site (code available at zenodo. org) (17). Individual-level data remained within host institutions, only aggregate results were provided to the research team. All the results are available for consultation on a regularly updated website as new databases and/or results are added (https:// data. ohdsi. org/Covid19CharacterizationCharybdis/). We report results by cohort and database. Demographics, cancer types, comorbidities, and outcomes are reported as proportions along with 95% confidence intervals (CI). To calculate these proportions, a minimum count required of 5 individuals was established to minimize the risk of re-identification of patients.
Lifetime cancer prevalence Overall, we identified 3,067,116 patients diagnosed and 572,300 patients hospitalized with COVID-19. The lifetime cancer prevalence range across databases was 4% to 25% in patients diagnosed; and 11% to 40% in patients hospitalized (Supplementary Table  S4 ). In addition, 274,557 patients hospitalized with seasonal influenza in 2017 to 2018 were identified (lifetime cancer prevalence range: 18%-39%). We included 366,050 patients diagnosed (Spain: 8,854; United States: 357,196) and 119,597 patients hospitalized (Spain: 2,610; United States: 116,987) with COVID-19 and cancer history; and 67,743 patients hospitalized (all from the United States) with seasonal influenza and cancer history.
Demographics The distribution of demographics, comorbidities, and outcomes of both COVID-19 cohorts can be found in Table  1  (95% CI of each condition available in Supplementary Table  S5 ). In the diagnosed cohort, patients were more commonly female (range: 53%-59%), aside from STARR-OMOP (47%) and VA-OMOP (7%). In contrast, in the hospitalized cohort, male slightly predominated in all databases (51%-60%, VA-OMOP: 96%), aside from HealthVerity and Optum-EHR (50% in both). Patients were mainly aged above 65 years in both COVID-19 cohorts but patients hospitalized were consistently older than those diagnosed (Supplementary Fig. S2 ). In the few databases reporting race, the proportion of African American patients was higher in the hospitalized cohort (9%-35%) than in the diagnosed cohort (6%-29%).
Cancer types For both COVID-19 cohorts, the frequency of each cancer type is reported in Supplementary Table  S6. The top 10 cancer types by frequency are reported in Table  2. In the diagnosed cohort, the most frequent cancers in four databases were breast (SIDIAP: 14. 2%; CU-AMC-HDC: 7. 3%; Optum-EHR: 6. 7%; and STARR-OMOP: 12. 3%) and prostate cancer (CUIMC: 6. 1%; HealthVerity: 12. 2%; IQVIA-OpenClaims: 6. 4%; VA-OMOP: 18. 1%). In all databases, non-Hodgkin's lymphoma (NHL) was among the five most common cancers. Bladder, colorectal, leukemia, and lung cancer were among the ten most frequent in at least seven databases. In the hospitalized cohort, prostate cancer was the most frequent cancer in all databases (equally with NHL in CU-AMC-HDC, 6. 4%); aside from Optum-EHR (second most frequent).
NHL was among the three most frequent cancers in all databases aside from SIDIAP (fifth     most frequent) and STARR-OMOP. Leukemia, liver and lung cancer were also within the top 10 in the majority of databases. We did not observe meaningful differences (i. e. , |SMD|>0. 1) when comparing cancer types between the diagnosed and the hospitalized cohorts (Supplementary Fig. S3 ).
Prior comorbidities In both COVID-19 cohorts, the most common comorbidities were cardiometabolic conditions, which were more frequent in U. S. databases (especially VA-OMOP) than in the Spanish SIDIAP database. For example, in the United States, the range of hypertension was 52%-87% (Spain: 32%) among diagnosed and 58%-93% (Spain: 33%) among hospitalized patients (Table  1 ). The prevalence of all the prior conditions summarized is shown in Fig. 1. Several comorbidities were more frequent among patients hospitalized compared with patients diagnosed (SMD>0. 1): heart disease and chronic kidney disease (all databases except STARR-OMOP); hypertension and type 2 diabetes (all except SIDIAP and STARR-OMOP; Fig. 2 ).
Thirty-day outcomes In the COVID-19 diagnosed cohort, hospitalization in the U. S. databases ranged from 14% to 35% (Spain: 25%) and occurrence of death from 2% to 10% (Spain: 14%). In the COVID-19 hospitalized cohort, outcomes were heterogeneous across databases. ARDS (range 8%-42%) was higher than 30% in three out of six databases (IQVIA-OpenClaims, Optum-EHR, VA-OMOP). Sepsis (6%-25%), cardiovascular disease events (7%-21%) and AKI (10%-17%) were also common. Thromboembolic events were less frequent (deep vein thrombosis: 2%-5%; pulmonary embolism: 2%-4%). Intensive services requirement ranged from 6% to 16%, whereas occurrence of death ranged from 6% to 26% in the United States (Spain: 21%).
Comparison of patients hospitalized with COVID-19 to those with influenza The characteristics of patients hospitalized with seasonal influenza and the frequency of each cancer type are reported in Supplementary Tables  S7  and S8 , respectively. Aside from VA-OMOP (96% male), the proportion of males ranged from 45% to 53%, and the majority of patients clustered around the ages of 60 to 85 years old (Supplementary Fig. S4 ). The proportion of African American patients was lower in the Influenza cohort than in the hospitalized COVID-19 cohort (Optum-EHR: 10% vs. 14%; VA-OMOP: 17% vs. 35%). When comparing the frequency of cancer types between patients with COVID-19 and influenza, we did not observe consistent differences across databases (Supplementary Fig. S5 ). The distribution of comorbidities was similar in both groups, with few exceptions (Fig.
3A ). For example, chronic obstructive pulmonary disease (COPD) was more common among patients with influenza in CU-AMC-HDC, Optum-EHR, and VA-OMOP (Fig. 4A ). Aside from CUIMC, outcomes were slightly more frequent in patients with COVID-19 in all databases. ARDS and death were meaningfully more frequent in patients with COVID-19. ARDS ranged from 16% to 42% (COVID-19) versus 14%-30% (influenza), with SMD>0. 2 in IQVIA-OpenClaims and Optum-EHR and SMD>0. 1 in VA-OMOP. Occurrence of death was higher among patients with COVID-19 compared with patients with influenza in Optum-EHR and VA-OMOP: 6% vs. 1% and 18% vs. 6%, respectively (SMD>0. 2; Figs. 3B  and 4B ).
Comorbidities should be taken into consideration when designing future studies assessing the effect of cancer on COVID-19-related health outcomes, as failing to adjust for some comorbidities or adjusting for others (over-adjustment) could lead to confounding and/or selection bias. In June 2020, the case-fatality ratio among confirmed COVID-19 cases was 11% in Spain and 5% in the United States  (30) , which is lower than the all-cause mortality observed in both cohorts in SIDIAP, CUIMC, and VA-OMOP. Undoubtedly, increased age and underlying comorbidities play a substantial role in COVID-19related mortality among these patients. However, mortality was remarkably lower in the database including cases as of October 2020, Optum-EHR (2% in patients diagnosed, 6% in patients hospitalized). These suggest that studies from the beginning of the pandemic, when testing was limited, might have overestimated mortality rates in patients with COVID-19, including those with cancer.
For instance, a meta-analysis including studies prior to July 2020, with data over 18,000 patients with cancer with COVID-19 (mostly inpatients), reported a pooled case mortality rate of 25. 6% (95% CI, 22. 0%-29. 5%; ref. 31) , which is in line with our results in the hospitalized cohort in CUIMC (26%) and VA-OMOP (18%) but higher than results in Optum-EHR. Finally, we compared patients with cancer history hospitalized with COVID-19 to those with seasonal influenza as a benchmark. We previously showed that patients with COVID-19 are more often male, younger and less likely to have respiratory and cardiovascular diseases than patients with influenza  (14). Interestingly, patients with COVID-19 and influenza with a history of cancer had a similar sex and age distribution and were of comparable health status.
Despite this similarity, patients with cancer history and COVID-19 had a higher occurrence of adverse outcomes than those with influenza. This study has several strengths, such as its large size. We have reported in a publicly available website more than 10,000 characteristics from over 300,000 and 100,000 patients diagnosed and hospitalized with history of cancer and COVID-19, respectively, using eight different databases. The diverse healthcare settings and populations described, together with our multinational approach, increase the generalizability of our findings. Further, we expect that more databases from additional countries will provide sufficient data on the cancer population as the pandemic evolves. By including only individuals with at least 1 year of observation time available, we have comprehensively captured baseline comorbidities, which could explain the higher prevalence of comorbidities in our cohorts.
In addition, we did not have information on socioeconomic status, ethnicity, nor race in most databases. We also lacked information on the cause of death and reported instead all-cause death. Third, the overlap between the diagnosed and hospitalized COVID-19 cohorts might have masked some differences in the prevalence of comorbidities between cohorts. Moreover, some patients might be included in more than one database (e. g. , in a hospital-based and claimsbased database from the United States). Unfortunately, we were unable to determine the degree of overlap across data sources because patient-level data was not shared for confidentiality purposes. Fourth, the differences found in the COVID-19/seasonal influenza comparison may have been influenced by temporal changes in clinical practice standards and coding.
Acknowledgments We would like to acknowledge the patients who suffered from or died of this devastating disease, and their families and carers. We would also like to thank the health care professionals involved in the management of COVID-19 during these challenging times, from primary care to intensive care units. This project has received support from the  European Health Data and Evidence Network (EHDEN)  project. EHDEN has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking (JU)  under grant agreement no. 806968. The JU receives support from the  European Union 's  Horizon 2020 research and innovation program  and  EFPIA. This research received partial support from the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC) ,  U.
S. National Institutes of Health (NIH) ,  U. S. Department of Veterans Affairs ,  Janssen Research & Development , and IQVIA. This work was also supported by the  Bio Industrial Strategic Technology Development Program  ( 20001234 ) funded by the  Ministry of Trade, Industry & Energy (MOTIE, Korea)  and a grant from the  Korea Health Technology R&D Project  through the  Korea Health Industry Development Institute (KHIDI) , funded by the  Ministry of Health & Welfare, Republic of Korea  (grant no. :  HI16C0992 );  Bill & Melinda Gates Foundation  ( INV-016201 ); and the  Direcci o General de Recerca i Innovaci  o en Salut from the  Department of Health of the Generalitat de Catalunya. E. Roel  is supported by  Instituto de Salud Carlos III  (grant no. CM20/00174 ). W. U. R. Ahmed reports funding from the  NIHR Oxford Biomedical Research Centre (BRC) ,  Aziz Foundation ,  Wolfson Foundation , and the  Royal College of Surgeons of England.