dataesr/scientific-paragraphs-categorization huggingface dataset:
the manuscript. A special thanks goes to  Lewis Anderson  for sharing his knowledge about conducting a systematic review. This publication arose from research funded by the  OX/BER Research Partnership Seed Funding Fund "Understanding Family Demographic Processes & In-Work Poverty in Europe. How Marriage ,  Parenthood , and Divorce Affect the  Risk of In-Work Poverty across the Life Course ", grant number  OXBER_SOC8 , as well as funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 681546  (FAMSIZEMATTERS). Antonino Polizzi acknowledges funding received by the  Clarendon Fund  and  Nuffield College.
Methods A total of 5319 five-year solid CCS diagnosed before the age of 21 between 1945 and 2000 in France were identified in the French Childhood Cancer Survivors Study cohort (FCCSS) and the French cancer registry. Information about health care expenditure was taken from the French national health data system between 2011 and 2016, and was described according to survivors' characteristics. Generalized linear models were used to determine associations between health care expenditures and survivors' characteristics.
Study population We used data from two sources, a multicenter cohort study (FCCSS) and the French Network of cancer registries (FRANCIM). The FCCSS is a retrospective cohort of 7,670 five-year CCS diagnosed for solid cancer or lymphoma (all malignancies except leukemia), before the age of 21 years between 1945 and 2000. Detailed information on the methods for data collection and validation has been already described  [11, 12]. FRANCIM includes all the population-based registries of cancer in France. This network records all newly diagnosed and confirmed cancer cases since 1975 in diverse areas of France  [13, 14]. The population covered by the FRANCIM's database represents 22% of the French population  [15]. Therefore, the FCCSS survivors who were included also in FRANCIM were considered as FCCSS patients.
Leukemia survivors from FRANCIM were excluded for better comparability with the FCCSS which did not include leukemia. From both data bases, we selected all five-year solid CCS diagnosed before January 2001 who were alive in January 2011 and who were linked to the National Health Data System (Système National des Données de Santé) (SNDS). Since no social security number is collected in the study, patients are identified within the SNDS by probabilistic matching with the full involvement of different French healthcare-related organizations: Caisse nationale de l'assurance vieillesse des travailleurs salaries (CNAVTS) is the third party for social security number reconstruction and Caisse Nationale d'Assurance Maladie des Travailleurs Salaries (CNAMTS) is the trust third party for SNDS health data gathering based on a non-identifiable number derived from the retrieved social security number.
The highly specific identification data that are provided to CNAMTS by the National Institute of Health and Medical Research (INSERM) are listed as below: family and first name, sex, date and place of birth and, unique arbitrary number. CNAMTS was in charge of both communication with CNAVTS and SNDS data extraction. The percentage of survivors linked to SNDS data after this procedure was 55. 6% (n = 3786) for FCCSS and 71. 9% (n = 2031) for FRANCIM survivors. In the present study, French national security number is not held by INSERM at any time. Survivors were followed throughout the SNDS until December 2016 or death. We excluded survivors who lived outside metropolitan France during follow-up due to the difference in the health insurance system, characteristics of the population and funding of care in French overseas territories  [16].
Data sources The SNDS is the health care claims dataset in France, which contains exhaustive individual data used for the billing and reimbursement data of the beneficiaries of the various national health insurance schemes which now covers more than 95% of the French population  [17, 18]. The SNDS is mainly composed by the outpatient healthcare consumption database (Données de Consommation Inter-Régimes database, DCIR) and the private and public hospital database (Programme de Médicalisation des Systèmes d'Information, PMSI) which is divided in four categories: Medicine, surgery and obstetrics hospitalizations (MCO), home hospitalizations (HAD), after-care and rehabilitation (SSR) and psychiatry (PSY). Although MCO and HAD had cost information since 2006, the availability of billing records for PSY and SSR systems began in 2011, thus we established this year as starting date when data was available for all systems.
Neuroblastoma was chosen as the referent for type of primary cancer variable since was one of the larger group of cancer of same histology. We compared the output of the model adjusted by all covariates (Model I), with models excluding variable of death (Model II) and patients who died (Model III). Finally, we performed several analysis by considering separately each type of expenditure and each type of cancer along with different interactions between cohort (FCCSS or French cancer registries) and type of cancer. Statistical significance was determined using p<0. 05. All analyses were performed using SAS 9. 4 software (SAS Institute, Cary, NC, USA).
Additionally, progressive disease or relapses more than 5 years after diagnosis of a brain tumor in children is common, particularly, in slowly evolving low grade tumors  [39]. Despite the fact that FCCSS survivors were treated in specialized centers to treat cancer, and thus could have been more adverse cases or received a more intense treatment, no significant difference in expenses were found at long term when adjusting on demographical factors and type of childhood cancer to others CCS in France. Although, survivors from FCCSS were younger at time of diagnosis and were recruited in early years than survivors from French cancer registries, they weren't particularly older during follow-up period. An advantage of our study is to have worked on a large sample of long term survivors. Also, we used a national administrative database which allowed accounting for comprehensive health care expenditures during six years using two sources of long-term CCS in France.
Another strength is the inclusion period starting in 1945, which allows us to study variations in cost across the age spectrum. However, our study is subject to some limitations. First of all, data for a cancer-free control group were not available, which limited our results to the CCS population. Secondly, FRAN-CIM is a network of population-based registries which does not have national coverage. Thirdly, we were unable to address the association between cancer treatments received by survivors and health expenditures, due to the lack of therapeutic information in survivors from FRANCIM. All in all, our findings provide a first estimation on annual expenditures and the economic burden of CCS in France by type of childhood cancer, and demographical characteristics of survivors. Additionally, transferability to other contexts may be limited to the France territory.
Acknowledgments The authors thank the staff of each member of the FRANCIM network who participated in the collection of data;  Claire Schvartz  ( Registre des Cancers Thyroidiens de la Marne et des Ardennes ),  Michel Velten  ( Registre des Cancers du Bas-Rhin), Anne-Valerie Guizard (Registre General des Tumeurs du Calvados ),  Guy Launoy  ( Registre des tumeurs digestives du Calvados ),  Anne-Marie Bouvier  ( Registre Bourguignon  des cancers Digestifs),  Anne-Sophie Woronoff  ( Registre des tumeurs du Doubs ),  Karima Hammas  ( Registre des tumeurs du Haut-Rhin), Brigitte Tretarre (Registre des Tumeurs de l'Herault ),  Marc Colonna  ( Registre du Cancers de l'Isère ),  Brigitte Lacour  ( Registre National des Tumeurs Solides de l'Enfant ),  Simona Bara  ( Registre des Cancers de la Manche ),  Clarisse Joachim  ( Registre de la Martinique ), Benedicte  Lapotre-Ledoux  ( Registre de la Somme ),  Pascale Grosclaude  ( Registre des Cancers du Tarn ), and  Florence Molinie(Registre de la Loire-Atlantique et Vendee).
Data collection and analysis The questionnaire link was distributed via e-mail to members of the platform Infectious Diseases International Research Initiative (https://infectdisiri. com/). Between July and November 2021, data were collected via the survey platform. The survey was entirely voluntary. An investigator collected the data, and the country and hospital names were coded to maintain anonymity for subsequent analysis. Google Drive was used to collect the responses/database solely to ensure data security. Repeated emails were sent to participating centers to confirm their responses and to fill in any gaps in data. The countries and intensive care units of the participants were classified by the "World Bank" and classified into 2 groups. 14 Group I comprised low-and lower-middle-income countries, while group II comprised upper-middle-and high-income countries.
Acknowledgments This research was funded by a public grant overseen by the  French National Research Agency  as part of the  Investissements d'Avenir program LIEPP  ( ANR-11-LABX-0091 ,  ANR-11-IDEX-0005-02 ) and the  Université de Paris IdEx  ( ANR-18-IDEX-0001 ). The author declares no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Remerciements : Nous remercions toutes les équipes des pharmacies d'officine ayant répondu à l'enquête, les pharmaciens des centres hospitaliers ayant une activité de cancérologie interrogés lors de la conception du questionnaire, la société Le Sphinx Développement pour la mise à disposition gracieuse du logiciel d'enquête. Nous remercions également le Syndicat des Pharmaciens de l'Aveyron ainsi que le groupement d'intérêt économique Pharmaveyron pour avoir contribué à la diffusion de cette enquête. Déclaration de liens d'intérêts : les auteurs déclarent ne pas avoir de liens d'intérêts. Supplément en ligneComplément électronique disponible sur le site Internet de Bulletin du cancer (https://doi. org/ 10. 1016/j. bulcan. 2022. 02. 007). G. Cavallier, M. Laudet, P-M Vayssettes, D. Balayssac, P.
A concept dictionary First, we need to structure and standardize clinical concepts that will be used as building blocks to create CUCs in the CKP. Heterogeneity and non-structured clinical concepts make centralization and interoperability difficult to achieve. Overcoming this barrier needs the integration of multi-terminology servers that provide mappings among the same concepts in various terminologies. We are integrating a custom implementation of the Health Terminology/Ontology Portal (HeTOP; URL: www. hetop. eu), which currently includes more than 100 terminologies and ontologies in 50 languages. We are also integrating the Observational Health Data Sciences and Informatics (OHDSI; URL: https://www. ohdsi. org) interrelated vocabularies provided by the international OHDSI network  [7].
Members of communities of practice work together to create content (CUCs). The content provided by a community could be open (every user can access the content) or close (only community members can access the content). The content could also be set as compensated access, i. e. , the users should have a license or subscription to access the content. Communities could belong to nonprofit or public organizations as well as health providers and commercial corporates. All the CUCs created in the CKP define the list and structure of concepts that should be used in the relevant clinical context in the EMRs. CKP does not provide or include the patient data. The patient data is stored and used in the EMRs.
Dressing layer 1 is the unloading material cut into alveoli that is in contact with the mattress, and dressing layer 2 is the UrgoStart Plus Border dressing that is in contact with the skin. Both layers were modelled as a cylindrical layer with a radius of 125. 00 mm. The thickness of dressing layer 2 was set to 3. 50 mm, whereas the thickness of dressing layer 1 was set to 5. 20 mm. A mattress with a height of 50. 00 mm was added to the model. The diameter of the model was 250. 00 mm to avoid boundary effects in the wound area. Symmetry in the sagittal plane was also considered so only half of the model was used for the simulation (Figure  2 ). All components were meshed with SOLID185 linear hexahedral elements ANSYS APDL (ANSYS 2020 R2 software, ANSYS Inc. , Cannonsburg, PA, USA) with a mixed pressure-displacement formulation for the soft tissues.
Methods We performed a scoping review of clinical papers published in PubMed in 2019 using AI techniques. We assessed differences in dataset country source, clinical specialty, and author nationality, sex, and expertise. A manually tagged subsample of PubMed articles was used to train a model, leveraging transfer-learning techniques (building upon an existing BioBERT model) to predict eligibility for inclusion (original, human, clinical AI literature). Of all eligible articles, database country source and clinical specialty were manually labelled. A BioBERT-based model predicted first/last author expertise. Author nationality was determined using corresponding affiliated institution information using.
Original search strategy and selection criteria The present study applies ML techniques to comprehensively review all medical and surgical (hereafter referred to as clinical) manuscripts published in PubMed through 2019 which employ AI techniques (defined here as either AI, ML, DL, NLP, computer vision [CV], or CNN). After identifying all such clinical manuscripts, we describe differences in both populations captured in databases and clinical specialty and in authors' nationality, gender, and domain of expertise to elucidate the extent of the disparities affecting AI in healthcare. Our review can best be defined as a scoping review. Scoping reviews are a novel approach well-suited for describing the mix of literature in a given area, in terms of the clinical specialties represented and the types of research questions being addressed  [26] [27] [28] [29] [30].
Machine learning model building to predict shortlist of eligible articles The PubMed search above identified 30,576 articles published in 2019. To build our training dataset, we randomly selected 2,000 articles and manually screened them for eligibility using Covidence review software  [32]. Two independent reviewers assessed the 2,000 articles for eligibility by checking (i) the manuscript title, (ii) the abstract, and (iii) the full text (in cases where eligibility was still unclear). Where the two reviewers disagreed about an article's eligibility, both manually cross-checked the full article again; a third reviewer's decision determined the final eligibility. Articles in PubMed missing either a title or an abstract were excluded from analyses, since there would not be sufficient information to characterize the machine learning model(s) being described in the paper.
A-posteriori manual review of the full texts revealed that such articles were almost exclusively commentaries, editorials, letters, and post-hoc analyses rather than full research manuscripts, confirming their ineligibility to be part of our scoping review. Using the subset of 2,000 labelled manuscripts classified into two categories (eligible and non-eligible), a machine learning model was trained to predict eligibility for the remaining set of 2019 PubMed search results for clinical artificial intelligence articles according to the abovementioned criteria. The trained model predicted the remaining articles to be either eligible or non-eligible. Our model leveraged transfer-learning techniques building upon BioBERT, a biomedical language representation model designed for biomedical text mining tasks  [33].
Extracting the source of each paper's database and its clinical specialty We determined the country from which the database was sourced by manually tagging two randomly generated subsamples of 300 papers each from the shortlist of 7,314 articles labelled as eligible. To properly label each instance, we independently reviewed the text (usually the methodology section or the data availability statement) or the supplementary material. In instances where the provenance of the database used to build the model(s) was not declared in either the manuscript or the supplementary material, or when it was imprecise (e. g. , "multiple countries throughout Eastern Europe"), the label was marked as missing. Upon completion of this task, the two reviewers compared their two manually labelled subsamples for consistency.
For each of the two subsamples, the two reviewers compared their labels; when needed, a third reviewer's decision determined the final specialty of the paper. If multiple specialties were deemed related, then multiple labels were assigned. Conversely, if no specialty was deemed relevant, no label was assigned. Similar to the process used to label the source of the database, the two subsamples of 300 papers were then combined to present the final results. The results of the manual labelling of each of the 300 subsamples can be found in Table  2 , and the overall combined results in Fig 2. A heat map was generated using the geopandas package, an open source project to work and plot geospatial data in Python (Fig  2B )  [35].
Machine learning model to predict each author's domain of expertise The domain of expertise of each author was determined using a similar approach to the identification of papers' clinical specialties. Specifically, a machine learning model was trained to predict the domain of expertise of the first and last authors for the entire shortlist of eligible articles. First, the domains of expertise of the first and last authors were manually labelled in the subset of eligible articles from the randomly generated 2,000 articles originally screened in Covidence. For this, two independent reviewers classified the first and last author as either (i) a data expert (i. e. , statistician), (ii) a domain expert (i. e. , a medical specialist or surgeon), or (iii) other. Results of the manually labelled subsamples were compared between reviewers to ensure consistency; a third reviewer made the final decision.
Second, we trained a classifier to predict the expertise of the first and last author among the three abovementioned categories. For this, we used transfer-learning techniques and fine-tuned an existing BioBERT model. The model was trained on the 1,852 (i. e. , 92. 6%) manually screened articles containing the considered author's affiliation as well as their background, following the same process than for the model built to predict the eligibility of articles. Next, the full model was fine-tuned. Lastly, we validated the model on the remaining 148 (i. e. , 7. 4%) manually screened articles. The corresponding code is available at Github (https://github. com/Rebero/ml-disparities-mit).
Approach to identify each author's nationality and gender For each publication, the list of authors and their corresponding affiliated institutions were acquired using Entrez Direct (EDirect), the suite of interconnected databases made available by the National Center for Biotechnology Information (NCBI) and accessible via a Unix terminal window  [36]. Given that the raw data contained the country of each research institution present in the list, the field was first tokenized and the resulting array of tokens was then parsed to retrieve country names. Each author was subsequently linked to the country in which their institution is based. Occasionally, some authors were affiliated with institutions spanning two or more countries. In those instances, all of their affiliated countries were mapped to the corresponding author.
Final shortlist of eligible clinical AI articles published in PubMed through 2019 The abovementioned PubMed search yielded 30,576 clinical AI articles published through 2019 prior to further screening for eligibility. Of the 2,000 articles manually screened, 368 (i. e. , 18. 4%) were found to be eligible. This labelled subset was subsequently used to train and test the model used to predict article eligibility. Our model achieved both a high Matthews correlation coefficient (MCC) of 0. 88 and a high Area under the ROC Curve (AUROC) of 0. 96 on the test dataset. In the case of a tie (i. e. , a predicted probability for eligibility of 50%), we biased the model to err on the side of classifying an article as eligible rather than ineligible. The objective was indeed to limit the number of articles that could be misclassified as ineligible while they should in fact be included (Fig  1A ).
Identification of the sources of the database(s) being used in the papers Of the two subsamples of 300 eligible papers each that we manually labelled, 254 and 251 (i. e. , ~84%) mentioned the sources of the database(s) being used, either in the manuscript or in the supplementary material (Table  1 ). The U. S. accounted for the majority of the data sources in both subsamples (48. 8% and 32. 7%, respectively), followed by China (11. 8% and 15. 5%, respectively). Other countries represented in both subsets, including the U. K. , Germany, Canada, and South Korea, had a substantially smaller prevalence (Table  1 ). Results emanating from the two labelled subsets were thus deemed comparable and combined; pooled results are presented in Fig 2A  and 2B. In sum, the U. S. contributed the vast majority of AI datasets published in 2019 (40.
Discussion Our study demonstrates substantial disparities in the data sources used to develop clinical AI models, representation of specialties, and in authors' gender, nationality, and expertise. We found that the top 10 databases and author nationalities were affiliated with high income countries (excluding China), and over half of the databases used to train models came from either the U. S. or China  [40]. While pathology, neurology, ophthalmology, cardiology, and internal medicine were all similarly represented, radiology was substantially overrepresented, accounting for over 40% of papers published in 2019, perhaps due to facilitated access to image data. Additionally, more than half of the authors contributing to these clinical AI manuscripts had non-clinical backgrounds and were three times more likely to be male than female.
Pre-training has grown primarily through NLP, where contextual word embedding models such as Word2Vec,  [41]  GloVe,  [42]  and BERT have improved performance of models across a variety of tasks  [43]. Pre-trained models are generally trained in one task and subsequently leveraged to solve similar problems. For example, BERT models were built using clinical notes from the publicly available MIMIC-III database for enhanced disease prediction,  [44]  and have since been used to improve clinical Japanese data representation written in other languages,  [45]  and integrated with other complementary medical knowledge databases  [46]. While development costs can be high (e. g. , training GPT-3, a novel large-scale language, cost over $10 million and required more than 300GB of memory),  [47]  such frameworks negate the limitations of working with smaller datasets through leveraging existing models for recalibration for diverse target populations.
Understanding how and why primary models are applicable to different cohorts (i. e. , with previously unseen data) also contributes towards refining initial model methodology and proposing robust model alternatives. The lack of diverse digital datasets for ML algorithms can amplify systematic underrepresentation of certain populations, posing a real risk of AI bias. This bias could worsen minority marginalisation and widen the chasm of healthcare inequality  [10]. Although understanding these risks is crucial, to date inadequate appreciation for them has occurred. A recent review of international COVID-19 datasets used to train ML models for predicting diagnosis and outcomes showed that of the 62 shortlisted manuscripts, half neither reported sociodemographic details of the data used to train their models nor made any attempt to externally validate their results or assess the sensitivity of the models.