dataesr/scientific-paragraphs-categorization huggingface dataset:
S. population upon whose data it was exclusively trained. Either outcome is disadvantageous to populations not represented in the large datasets commonly used to build these models. Increased computer power and massive dataset availability has driven the growth of ML in healthcare, especially over the last decade. The volume of data being generated cannot be understated-10 8 bytes in the U. S. alone, with annual growth of 48%-as well as increasing uptake of digital electronic healthcare systems and proliferation of medical devices  [50]. The ability of machines to interpret and manipulate data relatively-independent of human supervision/input brings with it the immense potential for improvements in healthcare quality and accessibility-particularly in resource-poor settings where specialist opinion is not as readily available  [51, 52].
Our analysis also revealed that the first and last authors of clinical AI papers published in 2019 were three times more likely to be men than women and marginally more likely to be data specialists rather than clinical experts. The underrepresentation of women in research, especially women of color, and in datasets used to inform AI is well-documented and discussed in detail elsewhere  [63, 64]. Historically, algorithms have only poorly accounted for differences in patient characteristics like biological sex, although individual-level factors are often involved in complex causal relationship patterns. For example, biological differences between patients can affect the metabolism and efficacy of certain pharmacological compounds and should be accounted for when predicting outcomes of treatment with a certain drug  [63].
Limitations Nonetheless, our study presents several methodological limitations. As a scoping review, our analysis includes data from papers published in 2019 alone. Although a great number of studies were included, we were unable to systematically assess temporal trends in clinical AI disparities. We measure these disparities by quantifying the lack of international representation in terms of both data sources and research authors but do not account for disparities that exist within countries. Future work must determine how AI/ML disparities might manifest locally and globally and identify ways to mitigate them, taking into consideration between-and within-country population differences. Moreover, classification by clinical specialty may be restrictive and not holistically capture the many fields in which model-derived findings can apply.
Conclusions Here, we demonstrate both population-and author-sources contributing toward bias in AI in healthcare. We find that U. S. and Chinese datasets and authors are disproportionately overrepresented in clinical AI. Image-rich specialties (i. e. , radiology, pathology, and ophthalmology) are the most prevalent among clinical AI studies published in 2019. Additionally, the authors of these papers are predominantly male researchers with non-clinical backgrounds. The rapid evolution of AI in healthcare brings unprecedented opportunities for accurate, efficient, and cost-effective diagnosis superseding the accuracy of clinical specialists, but with minimal human input-especially valuable in resource-poor settings where specialist input is relatively scarcer. Although medicine stands to benefit immensely from publicly available anonymised data informing AI-based models, pervasive disparities in global datasets should be addressed.
Data sources We use near-universe national administrative data for Denmark, France, Germany, Italy, Portugal and Spainsee Appendix F for a detailed description of each dataset. We drop agriculture and industries where the public administration is dominant. 10  We exclude selfemployed and household employees and only keep workers with at least 1 month of tenure with their current employer. The structure of the data varies across countries. In Germany and France, we observe all job matches (i. e. an employee matched with an employer) during the year, but not contract changes within a job match. 11  In Denmark and Portugal, we only observe job matches at one month of the year, namely October for Portugal and November for Denmark. In Italy and Spain, our observations are contracts rather than job matches.
26  We also know whether individuals work part time or full time and in which industry they are employed. 27  For new hires, we also know whether they were in employment the year before. In Portugal, we have information on the type of collective agreements each employer is subject to. This information is not available for other countries. However, for Germany we retrieve information on collective bargaining from the IAB Establishment Panel and match it with our data at the industry-level. 28 These data are averaged over time for each industry to improve representativeness. For comparability, information on collective bargaining coverage is aggregated at the industry level and over time also in the case of Portugal. In each country, we define four alternative indicators of collective bargaining coverage.
First, in each industry, we compute the proportion of employees covered by any type of collective agreement, i. e. either at the sector or at the firm level. Second, we define a dummy variable equal to 1 if an industry has a proportion of employees covered by any type of collective agreement larger than the median and 0 otherwise. Finally, we define two similar indicators based on the proportion of employees covered by sectoral collective agreements only. Although our measures of labour market concentration are constructed using the entire population, the regressions are conducted on random subsamples of the population in three out of six countries. 29  Descriptive statistics of our data are provided in Appendix  where 𝑖 indexes the worker, 𝑗 the establishment, 𝑓 the firm-by-municipality couple, 𝑙 the local labour market, 𝑠 the industry and 𝑡 is the year.
The share of spending on active labour market programmes in GDP amounted to 1. 65% in Denmark, 0. 68% in France, 0. 48% in Portugal and 0. 28% in Germany. The share of passive labour market policy spending (including unemployment benefits) was 1. 27% of GDP in Denmark, 2. 05% in France, 0. 88% in Germany and 1. 36% in Portugal. Source: https://www. oecd. org/employment/emp/ employmentdatabase-labourmarketpoliciesandinstitutions. htm. 37  In 2015, collective agreements covered 83% of the wage and salary employees in Denmark, 98% in France, 57% in Germany, and 74% in Portugal, while the OECD average was 33% -Source: OECD/AIAS Collective Bargaining Database (https://www. oecd. org/employment/ictwss-database. htm). countries we consider, but also why it is small when compared to what has been found in the United States 38 where sectoral collective bargaining does not exist and firm-level bargaining covers only 12% of employees.
Control variables include gender, education (4 categories), yearly dummies for workers' age, whether the individual was employed the year before hiring, whether he/she works full time or not, as well as firm-bymunicipality-by-year fixed effects, sector and establishment fixed effects (where different from firm-by-municipality fixed effects), and local labour market fixed effects. 𝐿𝑜𝑔(𝐻𝐻𝐼) is instrumented by the average of the log inverse number of firms in other FAs for the same occupation. The interaction between 𝑙𝑜𝑔(𝐻𝐻𝐼) and gender is instrumented by gender interacted with the instrument for 𝑙𝑜𝑔(𝐻𝐻𝐼). Standard errors are clustered at the labour-market-by-year level. -                    Each line in this dataset is a job match (establishment-by-employee-by-year). Establishments have a unique identifier which is invariant over time, except when the establishment changes location (or simply changes mail address), in which case it is assigned a new identifier.
Acknowledgements The views expressed here are only those of the authors and not those of the institutions to which they are affiliated or the OECD member countries. We are grateful to  Andrew Green ,  Luca Marcolin ,  Ioana Marinescu ,  Oskar Nordström Skans ,  Aaron Sojourner  and  Eliana Viviano  as Against these unique advantages, the dataset has the drawback that it does not record information about the hiring wage and the size of the firm.
Study design The study objectives and data collection methods were first explained to administration staff of the government schools, and after issuing administrative authorization, they were explained to teachers. An informed consent form was given to each teacher who accepted to participate in the study. Anthropometric parameters were measured and questionnaire forms were given to each participant. The teachers were asked to returned filled questionnaire within seven days upon reception. Teachers were assisted by research team members if questions were not well understood. Identification codes were assigned to each questionnaire to process and analyze it anonymously. Incomplete and poorly completed questionnaires were excluded from the final analysis. A total of 300 questionnaires were distributed, and 179 (60%) teachers returned correctly filled questionnaires (Figure  1 ).
Statistical analysis Qualitative variables were presented as percentages (%). Statistical analyses were conducted using the Statistical Package for Social Science v21. 0 (SPSS Inc. , Chicago, IL, USA). The normality of quantitative was checked using the Kolmogorov-Smirnov test. Pearson independence Chi 2 test was used to compare proportions of unpaired samples. Logistic regression analysis was used to determine factors associated with MSD among teachers. The association between the dependent variable (presence of MSDs) and independent variables (gender, age, PA) was quantified in logistic regression analysis by computing odds ratios (OR), their confidence interval at 95%, and p-value. The level significance was set for a value of p < 0. 05.
Procedure All tests were performed on a single day by two physiotherapists with at least two years of experience in neurology. During session 1, following the clinical testsisometric trunk strength, Modified Functional Reach Test (MFRT), Balance Assessment in Sitting and Standing (BASSP) and Timed Up and Go test (TUG) -, two raters tested unstable sitting balance with a FP. After 2 -4h of rest, rater 1 repeated the unstable sitting tests for session 2. Rater (1 or 2) and test (anteroposterior vs. mediolateral) orders were random. Since the FP is connected to a program which directly records data with no possible visualization, raters were blind to their own tests and to the other rater's.
Dynamic sitting balance on a seesaw The testing device consisted of a wooden bench, a FP (kin etools 2015, Kicarre company) and a seesaw (60 cm Â 35 cm with a height of 9. 5cm, Balance board 60 Pedalo ® ) (Figure  1 )  [24, 25]. The FP's dimensions were 60 cm Â 45 cm x 6. 2 cm; it consisted of two plates with four force transducers (SP4C3-MR, precision C3, HBM ® ) connected to a converter (NI USB-6009 DAQ, 14/8 inputs resolution, 48 ks/s) allowing rapid integration to a signal processing software (LabVIEW 2016). Before processing the signals, they were filtered with a 5 th Order Butterworth Low Pass Filter (cut-off set to 45 Hz). Subject was seated on the seesaw, feet flat on the floor with knees and hips in flexion. For each test, participants were instructed to be as stable as possible with their eyes open with both arms crossed over their torso.
26 An organism may become infected if a sufficient amount of viral particles interact with cells expressing both the TMPRSS2 protease and the ACE2 receptor and if the virus is able to hack into cellular mechanisms to produce and disseminate new virions. At each cycle, the virus needs to enter cell, repli-cate its RNA molecule, produce the proteins required for its sefl-assembly, and then be released. Omics database provide an overview of the tissues and cells types that express the TMPRSS2 protease and the ACE2 receptor in the different tissues of the human body (Human Cell Atlas). The commonly accepted hypothesis is that these cells constitute the first target cells for the SARS-CoV-2 virus. 27 The existence of a set of nasal cells in the expression map of the ACE2/TMPRSS2 suggests that the nasal epithelium is most often the first contaminated tissue.
Data collection Previous work in the UK  (Agyemang et al. 2015)  suggests that involvement of Ghanaian community leaders from religious groups and local key community members enhances study participation and could help promote understanding about the relevance of the study. Purposive sampling was therefore used to recruit key informants into the study from religious and ethnic groups using information sheets and mobile phone calls and through snowballing techniques. Other key informants were identified by the lead researcher (HO-K) through personal contacts within the Ghana Union Greater Manchester Branch. Key informants in this study were therefore religious leaders such as elders, pastors, leaders of Muslim groups, hometown leaders, chiefs, and a queen mother (a powerful female leader who plays an important role in local government), and leaders of the Ghana Union of Greater Manchester.
Data analysis All key informant interviews were recorded with permission from participants using a digital audio recording device. Interviews were transcribed verbatim and where necessary translated, reviewed for accuracy, and scored by two independent scorers (HO-K and RA) following the CRM handbook  (Plested et al. 2016). Prior to scoring, respondent validation was undertaken by discussing a sample of the transcripts for accuracy, and resonance with responses provided by two of the key informants via a phone call. Each transcript was read through thoroughly in full to gain a general idea of the content. For each of the five dimensions of the interview guide, there are nine anchored statement ratings. Each transcript was read systematically and compared to the most appropriate anchored statement in the handbook corresponding to a level of readiness on a scale from 1 (no awareness) to 9 (high level of community ownership).
Once all transcripts had been scored, the average score for each dimension was calculated and a score for each key informant was calculated. Then the overall average was calculated by adding the scores across all of the interviews and dividing by the number of interviews. To calculate the overall community readiness score, the average of the five dimensions was calculated. The overall mean score was then compared with the nine stages of community readiness as depicted in the CRM handbook (Fig. 1 ), to assess the level of readiness of the community. After scoring, thematic analysis was conducted using NVivo. To identify themes, a combination of inductive and deductive approaches was employed. HO-K conducted line-by-line coding to generate initial ideas that were later built into sub themes.
Strengths and limitations of the CRM model This study is the first to assess the stage of community readiness to prevent overweight/obesity among a Ghanaian immigrant community. The qualitative thematic data that complemented the quantitative scores has provided important contextual information that can be leveraged to plan, design and implement effective interventions for the Ghanaian immigrant community to tackle overweight/obesity in the future. While the views of the 13 key informants may be perceived as small and not be representative of Ghanaian community groups in Greater Manchester, each key informant was carefully selected to represent a distinct sub-group within the community, and the number of interviews in this study is in line with what is recommended in the CRM handbook. Also, given the disagreements in the literature about who community leaders and key informants are  (Marshall 1996; Poggie 1972) , participants were therefore selected from different ethnic and religious backgrounds, and varied in age and gender.
A draft protocol for the clinical validation was provided as part of the grant/funding application. Content in this publication reflects the authors' view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein. ISGlobal acknowledges support from the Spanish Ministry of Science, Innovation and Universities through the "Centro de Excelencia Severo Ochoa 2019-2023" Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program. Heleen Demeyer is a post-doctoral fellow of the FWO Flanders. Heiko Gaßner is supported by the Fraunhofer Internal Programs under Grant No. Attract 044-602140 and 044-602150.
Participants' flow through the study is documented and presented in a Consolidated Standards of Reporting Trials flow diagram  [41]. Reasons for dropout are recorded and data collected up to the time of withdrawal will be included in analyses unless the participant withdraws their consent. Participants. The four disease cohorts studied within Mobilise-D CVS are Parkinson's Disease (PD), Multiple Sclerosis (MS), Chronic Obstructive Pulmonary Disease (COPD) and Proximal Femur Fracture (PFF). Participants have to meet the core eligibility criteria as well as their respective cohort eligibility criteria. The full inclusion and exclusion criteria are listed in Table  1. The disease cohorts have been chosen because they represent different classes of mobility problems relating to low physical activity, different gait disturbances, and frailty, each affecting large groups of European citizens over substantial periods of time.
In the COPD cohort, patients with different COPD severity will be included, from those with mild disease and little burden of disease to those with very severe disease and significant burden and mobility impact. This represents the typical sample of patients recruited in clinical trials investigating pharmacologic and non-pharmacologic interventions. Exacerbations are events that punctuate the disease progression in COPD and their occurrence marks moments of acute deterioration and are often followed by only partial recovery. In the COPD cohort particular attention will be given to these events and how they interact with mobility in particular mobility decline. Mobilise-D will allow the study of mobility worsening over time in relation to other relevant clinical outcomes. In addition, the collection of treatment data will allow identification of patients undergoing pulmonary rehabilitation programs which offers insight in mobility improvement trajectories.
Outcome measures In order to meet the objectives of the CVS, digital mobility outcome measures as well as general and disease specific outcome measures (constructs) will be assessed. Digital mobility measures. Daily-living mobility performance and capacity will be assessed directly using wearable sensor devices. First, participants will wear the McRoberts MoveTest (McRoberts B. V. , The Hague, The Netherlands) to 'instrument' and objectively quantify physical assessments. Following the agnostic device approach proposed in the technical validation study  [36] , two different but metrologically equivalent devices will be adopted for the daily living monitoring. Accordingly, either the McRoberts MoveMonitor+ or the Axivity AX6 (Axivity Ltd, Newcastle Upon Tyne, UK) will be used to record free-living activity data over a period of at least seven consecutive days in an unsupervised condition after completion of the on-site assessment.
To describe the real-world walking behaviour we will extract walking behaviour metrics during the course of the day and week and model them using advanced statistical analysis such as cumulative probability function, detrended fluctuation analysis or entropy analysis. Data management. All study data will be transferred to a centrally managed data management platform. The Mobilise-D data management platform has been created on e-SC, an open-source cloud-based platform designed to provide secure ingestion, storage, sharing, and analysis capability for scientific studies (www. esciencecentral. co. uk)  [66]. Participant data is collected in a coded, de-identified manner, using electronic data capture as a default option. Data is either entered into the Mobilise-D data management platform directly or via third-party platforms (e.
g. , McRoberts or ERT). The proposed data flow model is illustrated in  Fig 4. Where electronic data capture is not feasible, paper case report forms are used (e. g. , falls diaries). Data from paper forms is entered into web forms on e-Science Central (e-SC), and the signed and dated paper forms will be scanned and uploaded to the platform. The Mobilise-D e-SC platform has been implemented using Amazon Web Services located within the European Union. Direct access will be granted to authorised representatives from the Sponsor, host institution, and the regulatory authorities to permit study-related monitoring, audits, and inspections-in line with participant consent. Clinical sites will be responsible for archiving all study documents for a period of time that is in keeping with institutional or national guidelines that pertain to that site.
Data monitoring Central monitoring of recruitment and data quality will be undertaken by the Mobilise-D Data Manager, as documented in Study Monitoring Plan. Participant enrolment will be regularly reviewed and compared against expectations for site and disease cohort. Where recruitment falls below a pre-defined percentage, mitigation plans identified by sites will be actioned. All data quality monitoring will be performed centrally, for example on data within study databases, uploaded documentation or by self-assessment checklists at site. Any data queries identified through central monitoring will be sent to each site in form of a monthly report. On-site visits will only be triggered if serious issues are identified at a site level. There is no non-serious adverse event reporting planned for this study.
Our low-cost technology has the promise to pervade clinical practice, with every healthcare centre able to offer inexpensive digital assessment systems, linked to a centralized system directly connected to healthcare databases. Our implementation of Mobilise-D will support advances in both therapy and care provision, with an opportunity for improved and more informed decision-making in assessing the efficacy of new interventions. Mobilise-D will provide the first-ever systematic approach to mobility determination that is standardised, codified, and freely available to industry, extending patient measurement systems that have utility across all aspects of patient care, from research through intervention development to routine large scale patient care. Mobilise-D will develop and publish a new generation of clinically validated, open-access algorithms, associated reference data, and a defined battery of relevant digital mobility outcomes that will allow any patient to receive a consistent and accurate mobility assessment from simple, low-cost devices.
Acknowledgments Full membership of the Mobilise-D consortium is available on the website https://www. mobilise-d. eu/partners. ISGlobal acknowledges support from the  Spanish Ministry of Science, Innovation and Universities  through the " Centro de Excelencia Severo Ochoa 2019-2023" Program  ( CEX2018-000806-S) , and support from the  Generalitat de Catalunya  through the  CERCA Program. Heleen Demeyer  is a post-doctoral fellow of the  FWO Flanders. Heiko Gaßner  is supported by the  Fraunhofer Internal Programs  under Grant No. Attract  044-602140  and  044-602150.