dataesr/scientific-paragraphs-categorization huggingface dataset:
Three of the centre datasets received (one for adolescents and two for children) were excluded from the analyses due to poor (<50%) response rates. • •• •• •• South Africa (Cape Town) H J Zar 84•4% August, 2017 15•2 •• •• •• •• Sudan (Khartoum) M Nour 99•9% March, 2017 14•1 •• •• •• •• Syria (Lattakia) In each centre, estimates of prevalence of symptoms for each age group were obtained by dividing the number of positive responses to each question by the number of completed questionnaires. If apparent inconsistencies were found between responses to a main question and a branched question (one dependent on the response to a main question), these were accepted and not recoded. For regional and global summaries, data for each centre were weighted by the sample size. We obtained country income categories from the World Bank, with countries categorised into low-income, lowermiddle-income, upper-middle-income, and high-income countries.
Outcomes and statistical analysis We sought a sample size of 3000 participants for each age group (with a minimum of 1000 deemed acceptable), a stringent requirement because of the number of hypotheses being tested. We examined changes in prevalence over time from ISAAC Phase I to GAN Phase I. We calculated the 10-year change in prevalence of symptoms for each centre using the difference between the two timepoints (eg, GAN Phase I and ISAAC Phase III) divided by the number of decades between the mean data collection dates of those timepoints. 2897 10•6% -1•4% -1•0 3•7% -4•8% -5•3 6•2% -1•3% -1•2 32•0% -1•3% -0•7 23•6% -2•5% -1•8 South Africa (Cape Town) 3979 21•3% 0•6% 0•5 16•6% 1•4% 1•5 12•0% 1•7% 2•7 36•0% 2•2% 1•3 41•4% 3•1% 2•4 Sudan (Khartoum) 1785 5•7% -4•8% -3•9 18•2% 1•9% 1•2 3•5% -2•7% -3•2 28•1% 9•6% 3•4 40•4% 14•4% 7•2 Syria (Lattakia) 1215 19•8% 7•7% 4•4 10•9% 2•8% 4•9 10•6% 4•7% 4•3 35•8% 13•6% 4•6 54•4% 19•4% 8•6 Region total 9876 15•1% 0•7% NA 12•4% 0•1% NA 8•6% 0•8% NA 33•4% 5•4% NA 37•6% 6•2% NA Americas Chile (South Santiago) 2750 13•4% -2•7% -3•2 13•7% -1•7% -2•4 3•9% -1•4% -3•2 16•7% -3•0% -3•5 32•9% -5•7% -5•6 Costa Rica (Costa Rica) 1338 20•8% -4•1% -3•9 22•0% -0•7% -0•6 9•4% -2•7% -3•7 14•4% -3•3% -4•4 31•2% 3•0% 2•7 Ecuador (Quito) 3000 6•3% -7•2% -6•5 4•5% -1•5% -2•5 3•0% -1•4% -2•5 18•2% 4•8% 3•3 28•0% 12•7% 9•4 (Table  2 continues     We derived estimates of the absolute 10-year rate of change in current wheeze, severe asthma symptoms, ever having asthma, exercise wheeze, and night cough for each centre.
Along with GAN Phase I centres who participated in ISAAC, we included ISAAC-only centres with data from both ISAAC Phases I and III  7  (94 centres with adolescents and 57 centres with children). This enabled us to estimate trends in prevalence across the full 27-year period (1993-2020) using all available time trends of centres in one mixedeffects linear regression model. Some centres had complete three-point time trend data available for the periods of ISAAC Phase I to ISAAC Phase III to GAN Phase I, whereas others had only ISAAC Phase III to GAN Phase I, ISAAC Phase I to GAN Phase I, or ISAAC Phase I to ISAAC Phase III. We included country-level and centre-level random intercepts in the mixed-effects linear regression models. The estimated time trend could therefore be interpreted as the within-centre absolute change in percentage point prevalence per decade.
Data from both age groups were included in the one model to improve model efficiency. We included age group as an a-priori confounder and effect modifier to assess time trends in each age group separately. We considered country income category and region as confounders and effect modifiers of the time trend. Therefore, our analyses controlled, by stratification, for age group, region of the world, and country income level. In all centres, boys and girls were approximately evenly distributed, so we did not control for sex. We also tested for evidence against a linear form for the time trend through the introduction of a quadratic term. The main models were additionally fitted to estimate the time trend in severe wheeze, exercise wheeze, night cough, and ever having asthma. To consider the effect of the level of prevalence of current wheeze on the change in prevalence of current wheeze, we additionally fitted separate models that included level of prevalence in ISAAC Phase III (or midpoint if no ISAAC Phase III data were available).
Results Data were available for 27 centres, in 14 countries in all four regions (Africa and Eastern Mediterranean, America, Europe, and South-East Asia and Western Pacific) that had completed ISAAC Phase I, Phase III, or both and for which GAN Phase I methods and data checks were completed by Jan 29, 2021. Overall, 119 795 participants from GAN Phase I were included: 74 361 adolescents in 27 centres (response rate 90%, range 68-100) and 45 434 children in 19 centres (response rate 79%, range 55-100; table 1; appendixes 2, 3). For the 27 centres in GAN Phase I with data available for adolescents, 13 had data from ISAAC Phase III alone, 13 had data from both ISAAC Phases I and III, and one (Athens) had data for ISAAC Phase I alone. For the 19 centres in GAN Phase I with data available for children, nine had data from ISAAC Phase III alone, nine had data from both ISAAC Phases I and III, and one (Chandigarh) had data from ISAAC Phase I alone.
Response rates were high. The study includes many centres from low-income, middle-income, and highincome countries from all regions of the world except North America. In about half of the centres, GAN Phase I was the first paired standardised study of asthma symptom prevalence; these included centres in five countries with no previous time-trend data (centres in Ecuador, Greece, Nicaragua, Sudan, and Syria), and nine centres that had no previous time-trend data (although different centres in those countries had provided ISAAC time-trend data). 8 ur study has some limitations, which include the smaller number of GAN Phase I centres compared with that of ISAAC Phase I to Phase III, which makes it difficult to generalise the findings to global prevalence changes. Centres were self-selecting, and thus not representative of countries except for Costa Rica, which was a whole country study.
Future GAN Phase I reports will contribute an estimate of the global burden of asthma, which will include the GAN Phase I centres in this Article as well as centres without time-trend data, and analyses in children and adolescents assessing rhinocon junctivitis and eczema and other asthma risk factors, similar to ISAAC Phase III reports, as well as studies in adults. Extensive risk factor analyses were undertaken in ISAAC Phase III. 29, 30 quivalent analyses will be undertaken with use of GAN Phase I data in conjunction with ISAAC Phase III data to determine to what extent changes in risk factor prevalences over time can account for the observed changes in prevalence and severity of asthma symptoms. We will also examine the relationship of management of asthma and symptoms. Abundant evidence exists that in many locations in the world, asthma management is suboptimal, contributing to a relatively high prevalence of severe asthma symptoms, 1,3 and there are strategies to improve this even in localities within low-resource settings.
Acknowledgments We thank the children and parents who participated in ISAAC Phases I and III and GAN Phase I, the school staff for their assistance and help with coordination, the principal investigators and their colleagues, and the many funding bodies throughout the world that supported the individual ISAAC centres and collaborators and their meetings. The  ISAAC International Data Centre  was supported by the  Health Research Council of New Zealand , the  Asthma and Respiratory Foundation of New Zealand , the  Child Health Research Foundation , the  Hawke's Bay Medical Research Foundation , the  Waikato Medical Research Foundation ,  Glaxo Wellcome New Zealand , the  New Zealand Lottery Board , and  AstraZeneca New Zealand. Glaxo Wellcome International Medical Affairs  supported the regional coordination and the  ISAAC International Data Centre.
The  GAN Global Centre in Auckland  was funded by the  University of Auckland  with additional funding from The  International Union Against Tuberculosis and Lung Disease ,  Boehringer Ingelheim New Zealand , and  AstraZeneca Educational Grant. The London Data Centre was supported by a PhD studentship (to CER) from the  UK Medical Research Council  (grant number  MR/N013638/1 ) and funding from the  European Research Council (ERC)  under the  EU's Seventh Framework Programme  ( FP7/2007-2013 ,  ERC  grant agreement number  668954 ). The Murcia Data Centre was supported by the  University of Murcia  and by the  Instituto de Salud Carlos III  ( PI17/0170 ). We thank the  National Institute for Health Research (NIHR) Global Health Research (GHR) Unit on Lung Health and Tuberculosis in Africa at Liverpool School of Tropical Medicine (IMPALA)  for helping to make this work possible ( 16/136/35 ); IMPALA was commissioned by the  NIHR (GHR ) using UK aid from the  UK Government.
The views expressed in this publication are those of the authors and not necessarily those of any of the funders. Individual centres involved in GAN Phase I data collection were funded by the following organisations:  Costa Rica  and  Nicaragua  were partly funded by an unrestricted grant from  AstraZeneca  for logistic purposes; in Kottayam,  New Delhi, Chandigarh, Bikaner, Jaipur, Lucknow, and Pune (India) , GAN Phase I was undertaken by  Asthma Bhawan , which was supported by  Cipla Foundation ;  Puerto Vallarta Centro Universitario de la Costa (Universidad de Guadalajara, Mexico), Auckland Asthma Charitable Trust (New Zealand), and Ibadan (Nigeria)  were funded by  NIHR  ( IMPALA grant   16/136/35 ) using UK aid from the  UK Government  to support GHR;  Cape Town  (South Africa) was supported by the  SA Medical Research Council and Allergy Society of South Africa ; Lattakia (Syria) was supported by  The Medical National Syndicate.
Data sharing ISAAC data are already deposited for wider use. The study protocol, including a recommended informed consent form and statistical analysis plan, are in the public domain (https://beta. ukdataservice. ac. uk/datacatalogue/studies/study?id=8131). The GAN Phase I data, including de-identified individual participant data, will be made available on the GAN website within 12 months of the publication of all GAN Phase I analyses. Access will require a formal request through the website, a written proposal, and a signed data access agreement.
ACKNOWLEDGEMENTS This position paper was initiated by the  ESMO/SIOPE Cancer in Adolescents and Young Adults Working Group. The members of this WG sincerely thank the  ESMO  and  SIOPE leadership  for their support in this manuscript and our WG activities. Manuscript editing support was provided for a previous version by  Angela Corstorphine of Kstorfin Medical Communications Ltd ; this support was funded by  ESMO. FUNDING The European Society for Medical Oncology (ESMO)  (no grant number) and the  European Society for Paediatric Oncology (SIOPE)  (no grant number) were the legal sponsors of this position paper. No research funding for the meetings or manuscript preparation was received from any third parties. DISCLOSURE DS reports receipt of research grants from  Teenage Cancer Trust. FAP reports personal financial interest as a Scientific Director at the  European School of Oncology ; receipt of lecture/presentation fees from  Prime Oncology and Takeda ; receipt of honoraria for advisory board participation/advisory services from  Roche ,  AstraZeneca ,  Clovis and Ipsen.
Sample Size Determination. TEARS EVOKE SOCIAL SUPPORT INTENTIONS Based on a non-systematic literature review, we identified the warmth effect as the smallest main effect  (d =. 45 [. 33,. 58 ], see Supplementary Material, Figure  1. 2. 2). Using the simr package  (Green & MacLeod, 2016)  in R (R Core Team, 2018) and the multilevel model obtained from our pilot study (Main Pilot 3), we performed a power simulation (alpha level at. 05). The pilot study sample size, which included 71 participants (279 cases), had a post-hoc power of 1. We, therefore, decreased the sample size until we reached a stable simulated power of. 95, which was reached with a total sample of N = 50 (total number of cases 200 given four repetitions per participant). In order to account for possible exclusions and crosscultural variability of the effect size, we aimed to include a minimum of 80 participants (320 cases) per sampling location.
All data were stored on a local server at the University of Oslo and will be made publicly available upon publication at the project page (https://osf. io/fj9bd/). Pilot Studies. We performed several pilot studies in order to examine the effectiveness of the design and the stimuli. First, we tested and confirmed whether the vignettes accompanying our tearful and non-tearful stimuli were perceived as positive, negative, or neutral (Supplementary Material 2. 1 & 2. 2 -Situation Ratings). Afterward, we tested a mixed design but found that our main manipulation did not work as intended (because the tears were not visible enough; Supplementary Material 2. 4 -Main Pilot 1). We updated the materials (Supplementary Material 2. 5) and tested the revised stimulus set in a within-subjects design. After revising our main design, we performed three additional pilot studies in order to get a further basis for a power analysis for our main study (Supplementary Material 2.
Main Stimuli. We employed a total of 100 different stimuli that represent five different 'ethnic' groups (as characterized by the respective databases): White, Asian, Black, Latinx, and Turkish. We randomly chose 20 stimuli from each group representing ten females and ten males. All individuals showed a neutral expression, 12  as we were specifically interested in the effect of tears and wanted to control for any facial expressions associated with emotional crying. Stimuli including individuals of European, Asian, African American, and Hispanic descent, were taken from the Chicago Face Database  (Ma et al. , 2015). Pictures of Turkish individuals from a Mediterranean, Middle Eastern, or Balkan background were taken from the Bogazici database  (Saribay et al. , 2018). For each picture, tears were digitally added using a procedure developed by  Küster (2018a;  see Figure  1   This technique has been successfully employed in previous studies (e.
Translation. TEARS EVOKE SOCIAL SUPPORT INTENTIONS Translations were performed using a five-step back-translation method modeled on the PSA guidelines  (Moshontz et al. , 2018). First, a bilingual person translated the material from American English to the target language. Then, another bilingual person translated the resulting material independently back to English. Subsequently, translators discussed similarities and differences in the two versions with a third bilingual individual. The resulting preliminary version was given to two non-academics fluent in the target language that reported perception and possible misunderstandings. After making cultural adjustments, the final version of the translation was produced. Note that some language versions were used for several countries (e. g. , Latin America).
Results For all analyses, we set the alpha level at. 05. 14 We analyzed the data employing multilevel models and the lme4 package  (Bates, Mächler, Bolker, & Walker, 2015)  in R (R Core Team, 2018). 15 We report unstandardized effect sizes B and their 95% confidence intervals, standardized effect sizes d, and overall effect sizes R 2 (Page-Gould, 2016) based on the sjPlot package  (Lüdecke, 2018). 16 For the main models, we always added participants nested in countries, targets nested in ethnicities as random effects, and allowed their intercepts to vary randomly  (Judd, Westfall, & Kenny, 2012 ). An overview of all registered models is presented in the Supplementary Material 4. 1. To examine effects across countries, we employed random-effects meta-analyses using the metafor package  (Viechtbauer, 2010).
In general, we performed equivalence testing to determine whether effects are smaller than an effect size we a priori consider to be interesting (because in large samples like ours, many very small effects will still be significant,  Lakens, 2017). We set the smallest effect size of least interest (SESOI) to d = +/-. 20 and used the TOSTER package to test for equivalence. 17 Given our final sample size, even very small effects were likely to attain statistical significance. With the equivalence test, we evaluated if the minimal effects are very small (statistically significantly smaller than d = |0. 20|), and if they were, we did not interpret them. 14 We realized later that we did not register to correct our alpha given the amount of hypotheses tested. In general, even when setting the alpha at.
001, interpretation of our findings would have remained the same. For the main confirmatory analyses, we present adjusted p-values using the Holm correction. 15 In case models did not converge, we employed the Nealder Mead optimization. Note that this decision was not registered. 16 Note that we originally registered to calculate effect sizes "based on transformations by  Bowman (2012)  and  Lakens (2013). " We now employ the sjplot package for simplicity. Results of these calculations differed to a non-substantial degree. Note that effect sizes obtained by the sjplot package differed slightly from the metaanalysis approach, as the latter did not take participant random effects into account. 17 In the main manuscript we only report cases in which the effect size was statistically equivalent to zero.
Measurement Equivalence. The topic of measurement equivalence is of high importance in cross-cultural research (Van de  Vijver & Tanzer, 2004). It tries to address the question of whether measures are completed similarly across different languages and cultures and is an important prerequisite for comparing effect sizes or mean ratings. However, adequate model fit for strict or scalar equivalence, referring to equal intercepts, thereby allowing the comparison of mean scores, has low practical applicability especially given a high number of countries as in the present project  (Byrne, Shavelson, & Muthén, 1989). Therefore, we tested for partial measurement equivalence for the main outcome measure (intention to support) across countries using the semTools package  (Jorgensen et al. , 2018). We observed an adequate model fit for the metric solution (CFI =.
24 To reduce overfitting, we used a split-half cross-validation technique by randomly dividing the full dataset into two halves  (IJzerman et al. , 2018). Before running the algorithm, we checked for extreme effect sizes using the robust median absolute deviation  (Leys et al. , 2013)  and identified the effect from the United Arab Emirates as an extreme point, which in turn was removed for these analyses. On the first half of the data, we employed a random forest algorithm for meta-analyses using the MetaForest package  (Van Lissa, 2020). Random forest represents a supervised machine learning approach that has several strengths compared to classical regression analyses as it is naïve to the direction of effects, can include higher-order interactions, is non-parametric, and can overcome problems with multicollinearity (see  IJzerman et al.
TEARS EVOKE SOCIAL SUPPORT INTENTIONS 4. 4. 10. We found that variables including the human development index, social support, a country's GDP, extraversion, and subjective well-being showed the highest variable importance, while moderators such as historical heterogeneity of migration, the amount of urban population, life expectancy, or climate demandingness showed a negative importance. For the second half, we ran several meta-regressions using only the predictors indicating a higher variable importance than zero from the first half dataset one-by-one. We inspected the amount of heterogeneity explained by the combined and individual moderators. We set our alpha level at. 005. An overview of moderators and their contribution by decreasing order of variable importance is provided in Table  5.
Therefore, it is possible to TEARS EVOKE SOCIAL SUPPORT INTENTIONS assume a common basis associated with responses when observing other people crying. This could be based on evolutionary aspects, as discussed by  Walter (2006) , or simply refer to social scripts that are embedded in all of the tested countries. It is intriguing that humans probably are the only species that produce tears when crying  (Vingerhoets, 2013). The universality of this effect of tears in observers is consistent with theories by  Hasson (2009)  and  Walter (2006) , who argued that, through natural selection, the secretion of visible tears was favored as it signals the need for help, thereby instigating bonding and interpersonal connections. Hasson and Walter argue that it may be that tears are one factor contributing to the development of humans as an ultrasocial species.
Acknowledgements While working on the study and/or writing the present paper  Krystian Barzykowski  was supported by the  National Science Centre, Poland  ( 2015/19/D/HS6/00641 ,  2019/35/B/HS6/00528 ) and by the Bekker programme from the  Polish National Agency for Academic Exchange  (no. :  PPN/BEK/2019/1/00092/DEC/1 );  Patrícia Arriaga  and  Irina Konova  were supported by the  Portuguese Foundation for Science and Technology  ( UID/PSI/03125/2020 ). Gyöngyi Kökönyei  and  Natália Kocsel  were supported by the  Hungarian National Research, Development and Innovation Office  ( FK128614 ) and  Gyöngyi Kökönyei  was supported by the  Hungarian Brain Research Programme  (Grant No. 2017-1. 2. 1-.
Literature Search We reviewed all studies reporting on the effect of home-based exercise training on nonspecific LBP (i. e. , LBP not consecutive to a specific pathology such as inflammatory, tumoral or infectious process)  [3]. Animal studies were excluded. The PubMed, Cochrane Library, Embase and ScienceDirect databases were searched until 20 April 2021, with the following keywords: low back pain AND (exercise OR physical) AND home (details of the specific search strategy used within each database are available in Appendix A). The search was not limited to specific years and no language restrictions were applied. To be included, articles were required to simultaneously meet the five following inclusion criteria: (1) randomized controlled trials (RCTs); (2) population ≥ 16 years old; (3) with non-specific LBP (chronic or not); (4) evaluation of at least one of our main clinically relevant outcome (i.
Data Extraction The data collected included: (1) characteristics of the study, including the first author's name, publication year, country and continent, study design, outcomes of included arti-cles, and number of participants; (2) characteristics of individuals, such as the mean age, sex (percentage of males), weight, height, and body mass index, percentage of smokers and regularly physical active individuals, education and marital status, and duration of complaints; (3) characteristics of the intervention, such as whether the intervention was supervised (totally supervised/partially supervised/not supervised), standardized or individualized (partially or fully), the type of intervention (education, aerobic exercise, stretching, strengthening, relaxation, postural exercise, yoga, other exercises), the frequency and duration of sessions, the duration of the program, and the location of training (home or other setting); and (4) characteristics of our main outcomes, such as the type of assessment of pain intensity and functional limitation, and measures (mean and standard deviation) before and after the training.