dataesr/scientific-paragraphs-categorization huggingface dataset:
Search Outcome and Bibliographic Overview The detailed selection process is illustrated in Figure  1. After removing duplicates, 2964 articles were identified through the database search. The screening of titles and abstracts led to the assessment of 518 articles in a full-text review. In total, 152 original articles and 13 review articles met our inclusion criteria. The manual assessment of the references from the review articles added 26 original articles. Only original articles of those reviews that met our inclusion criteria were included in the final selection of this scoping review. Finally, 178 articles were included in this scoping review. The main characteristics of the articles included in this review are presented in Table  1 , and 178 complete references are presented in Multimedia Appendix 3  [6, 8, 11, 18, 19,.
• Public databases  [53, 158]  • Websites  [41, 53, 69, 83, 88, 90, 127, 138, 141, 181]  Probably the socioeconomic group odds are they don't have money to buy these fun things, or they don't have the education to be able to use them. So they're left in the • Web-based FAQ a section  [88, 103]  dark, and they're probably the ones that are most easily taken advantage of" (oncology patient  [114] ).
When Individual Results Become Available The return of individual results and the management of clinically important incidental findings have been largely discussed in the context of biobanking, but appear important in other contexts, including administrative databases  [54, 97] , digital data  [153] , and mobile apps  [81]. Some stakeholders highlighted how these communications-whether generic when global research results become available or specific when new individual research results become available-were deemed important to them as they would permit them to act on their own health or the health of their communities (Table  5 , row 2). Finally, a few stakeholders deemed it necessary to communicate in the case of a breach of confidentiality.
Exemplary projects and patient portals include the tailored, ongoing communication of the CHRIS longitudinal study  [202] , NIH-funded All of Us Research Program  [219] , Connect Care (Canada) [220] that allows bidirectional interaction with the patient, Sundhed (Denmark)  [221] , and Health data hub (France)  [222]. Finally, ongoing innovations in data security will be important to enhance privacy and trust in all these initiatives. Examples include CrowdMed, a blockchain approach proposed by Shah et al  [180]  and MiNDFIRL, a software to enhance privacy in secondary database studies  [103].
Limitations Our search yielded most articles originating from North America and Europe and documenting Western contexts and attitudes; therefore, our review may not entirely represent other societies. In addition, although we cited research conducted with stakeholders from Indigenous communities, we cannot conclude that the views collected are representative of all Indigenous communities. Furthermore, many earlier articles focused on research conducted in the context of biobanking. We restricted our analysis to articles that explored stakeholder perspectives with a focus on the future use of samples or data. During thematic analysis, we were able to conclude that many of the perspectives overlapped with research conducted in other contexts, such as administrative databases, data mining, and mobile app.
Acknowledgments The authors would like to express their sincere thanks to  Professor Daniel Caron  and  Emmanuel Bilodeau  for their critical review of the manuscript draft. They also want to thank  Dominique Wolfshagen  for creating Figure 2,  Timothey Bédard  for his important work in the coding of the articles, and  Denis Boutin  and  Karina Prévost  for their thoughtful and wise recommendations as patient partners in the research program. This work was supported by J-FE personal fundings.
Results EFA yields a one-dimensional score combining four items with a 0. 7 Cronbach's alpha indicating good internal consistency. The score is strongly associated-p-values significant at the 5% level-with determinants of healthcare-seeking (including, but not limited to, sex, education, marital status, poverty, and distance to the health facility). Additionally, the score can predict non-utilization of primary care at the household level, utilization and nonutilization of primary care following an individual's episode of illness, and utilization of primary care during pregnancy and birth. These results are robust to the use of a different dataset.
Data The CMUtuelleS dataset contained rich self-reported micro-level data on the individuals and their households. In addition to standard socio-demographic variables, including, age, education level, sex and marital status, data reported GPS coordinates, which were used to compute distances between the household and the nearest health facility and CBHI office, respectively. The survey recorded the participants' health insurance status and self-reported health (12-Item Short Form Survey questionnaire  [27] , chronic illness, and handicap). Additionally, participants reported perceived quality of care at the local healthcare facility, knowledge of community-based health insurance, willingness to pay for health insurance, risk aversion  [28] , and generalized trust  [29]. The survey also extensively quantified the household's expenditures (including monthly consumption expenditures per adult equivalent, and out-of-pocket health expenditures) and included several measures of poverty (specifically, monetary, food, and subjective poverty).
Confirmation in an alternative data set The score was computed with confirmatory factor analysis (CFA)  [34]  in the dataset of the ANRS12356 AmBASS survey using the same set of items selected by EFA in the CMUtuelleS dataset. The AmBASS survey was conducted in the Niakhar HDSS in 2018-2019 and featured a sample representative of the general population living in the area  [35, 36]. Its dataset contains information on determinants of healthcare-seeking as well as self-reported and observed primary care utilization, which allowed to assess construct and predictive validity of the score in the general population living in the Niakhar HDSS. For sensitivity analysis purpose, the score was also re-built using stepwise descendant EFA to investigate potential differences in the structure of the score (i.
Confirmatory dataset In the AmBASS dataset, items (3)-(  6 ) (hereafter 'reduced PBMC score') yielded a 0. 71 Cronbach's alpha indicating good internal consistency. Stepwise descendant EFA conducted in the AmBASS dataset yielded a one-factor score with the full set of items (hereafter, 'full PBMC score'). The CFA model estimated on items of the reduced PBMC score fitted the data well-as indicated by goodness-offit measures, which got worse when computed on the full set of items. Both the reduced and full PBMC scores were significantly associated with determinants and healthcare seeking (e. g. , education, age, residency, resources), and primary care utilization. These results confirm the construct and predictive validity of the four-item PBMC score in the general population of the Niakhar HDSS, but suggest that the structure of the score may be sampledependent.
Conclusion We used DHS-based items on perceived barriers to medical care to build a one-dimensional score in both men and women living in rural Senegal. This PBMC score is internally consistent and confirmed in the Niakhar HDSS using a different dataset representative of adult individuals living in the same area. The score is significantly associated with a wide range of determinants of healthcare-seeking (including, but not limited to, sex, education, marital status, poverty, and distance to the health facility). Additionally, the score can predict non-utilization of primary care at the household level, utilization and non-utilization of primary care following an individual's episode of illness, and utilization of primary care during pregnancy and birth. The score was confirmed using CFA in the general adult population living in the Niakhar HDSS, though further investigation is warranted to confirm its validity in other settings.
Acknowledgements We are grateful to all study participants, the staff at the  Niakhar Health and Demographic Surveillance System , and the members of the  UNISSAHEL program. A complete list of members of the  UNISSAHEL Study Group  is given in Appendix A1 in the Supplementary Material. We also thank  Bruno Ventelou ,  Sylvie Boyer , and  Mohammad Abu-Zaineh , who co-led the  UNISSAHEL economic research program. Our thanks also go to  Andrainolo Ravalihasy ,  Richard Lalou ,  Jean-Yves Le Hesran ,  Bruno Boidin ,  Jérôme Wittwer ,  Fatoumata Hane  and participants to the AMSE PhD seminar, the  UCL Global Health Brown Bag seminar , and the 2022 European Health Economics conference for their questions and suggestions. We are also thankful to two anonymous reviewers for their valuable comments.
Funding This research is part of the  UNISSAHEL program  ( Universal Health Coverage in Sahel ), funded by the  Agence Française de Développement (AFD). This work also received support from the  French government  under the France 2030 investment plan, as part of the  Initiative d'Excellence d' Aix-Marseille Université -A*MIDEX -Institute of Public Health Sciences of Aix-Marseille  ( AMX-20-IET-014  and  ANR-17-EURE-0020 ) managed by the  French National Research Agency.
Background A national framework for population-based cancer registration was established in Russia in the late 1990s. Data comparability and validity analyses found substantial differences across ten population-based cancer registries (PBCRs)in Northwest Russia, and only four out of ten met international standards. This study aimed to assess the completeness of the PBCR data of those registries. Methods Qualitative and quantitative methods recommended for completeness and timeliness assessment were applied to the data from ten Russian regional PBCRs in Northwest Russia, covering a population of 13 million. We used historic data methods (using several European PBCRs reference rates), mortality-to-incidence ratios (M:I) comparison, and death certificate methods to calculate the proportion of unregistered cases (Lincoln-Petersen estimator and Ajiki formula).
Results Incidence rate trends of different cancer types were stable over time (except one region -Leningrad oblast). A slight drop in incidence rates in older age groups for several sites in the Northwestern regions was observed compared to the reference from European countries. Comparing M:I ratios against five-year survival revealed systematic differences in Leningrad oblast and Vologda oblast. Assessment of completeness revealed low or unrealistic estimates in Leningrad oblast and completeness below 90% in St. Petersburg. In other regions, the completeness was above 90%. The national annual report between 2008-2017 did not include about 10% of the cases collected later in the registry database of St. Petersburg. This difference was below 3% for Arkhangelsk oblast, Murmansk oblast, Novgorod oblast, Vologda oblast and the Republic of Karelia.
In countries with developed modern healthcare systems, PBCRs collect information through different sources. For example, In Norway, data are provided by hospitals, pathological laboratories, general practitioners, and the National Statistics Office (death certificates)  [6]. In Finland, PBCR receives notifications from multiple sources: hospitals, physicians, pathology and haematology laboratories, the Population Register Centre, and the National Statistics Office  [7]. In Bulgaria, PBCR receives information from notifications of new cancer cases, hospital discharge records, pathology reports, other diagnostic laboratory results, and death certificates  [10]. In Ukraine, not all regions had access to cause of death information  [12] , and some quantitative methods for completeness could not be applied.
In Hungary, the death certificate information is available from the National Statistics Office for 85-90% of cases. However, cases unique to death certificate databases are not recorded in the PBCR database  [13] , making some quantitative methods for completeness assessment not feasible. The access to sources of information defines PBCR's ability to apply different methods and assess cancer registry completeness. While several sources are virtually available in Russia  [5] , two sources can be reliably and explicitly identified in the PBCR database: clinical notifications and death certificates, allowing for quantitative completeness assessment. Still, it has not been performed before Incomplete cancer registry data adds bias to cancer burden assessment. Cancer incidence and mortality trends analysis in Russia  [14, 15]  that guides national cancer control policies may suffer from incomplete registry data.
Methods This report follows recommendations for completeness assessment and applies several qualitative and quantitative methods  [4]. Completeness is the degree to which the PBCR covers all of the incident cases in the target population. Qualitative and semi-quantitative methods measure the degree of completeness relative to other registries and sometimes overlap with validity methods. These include the historic data methods (stability of incidence rates over time, comparison of incidence rates with similar populations, the shape of age-specific curves, and incidence rates of childhood cancers) and mortality-toincidence ratios (M:I). Quantitative methods provide a numerical evaluation of completeness in percentages and include capture-recapture and death certificate methods.
Data We analysed data from the ten PBCRs databases of eleven regions of Northwest Russia (the Arkhangelsk oblast (including the Nenets Autonomous Okrug), the Murmansk oblast, the Republic of Komi, the Republic of Karelia, the Pskov oblast, the Kaliningrad oblast, the Leningrad oblast, the Novgorod oblast, the Vologda oblast, St. Petersburg) extracted in December 2019  [5]. The current state and procedures of cancer registration in Russia were in-depth described in one of our previous reports  [2]. In brief, regional cancer registries established in all federal regions collect information on all malignant and in situ neoplasms (ICD-10 codes C00-96 and D00-09) from notification forms that all physicians and hospitals must provide to PBCR. PBCRs are also entitled to regularly link records with mortality records to follow up on the vital status.
In practice, PBCRs are often part of a regional cancer hospital, and data collection can also (but not necessarily) involve the local hospital registry. The information should be reported by all other regional medical facilities in the region in the form of paper notifications. Information transfer may not be performed in case of migration or death in different regions. Our previous report described regions in Northwest Russia  [5] , but we obtained additional information about cancer registries in the regions (Table  1 ). Registries were established during the 1990s except in Novgorod Oblast (2003), Vologda Oblast (2005) and Arkhangelsk Oblast  (2000). Most of the registries were based in the regional cancer hospitals (Oncology Dispensaries), except Saint-Petersburg and Kaliningrad Oblast, where registries are part of the regional medical statistics service.
All the regions somehow established the mortality linkage with mortality data. However, it was manual in half of them. In other words, cancer registry personnel or regional oncologists manually reviewed death certificate records at civil registries to detect 1) cancer patients from the registry who have died and 2) death certificates that mention cancer. The semi-automatic procedure involves searches in electronic databases of the state insurance system (Obligatory Medical Insurance) and/or civil registries. Active traceback of notifications based on death certificate only (DCN) was reported in 6 out of 10 PBCRs. PBCRs data were subject to the multistep conversion and cleaning procedure using "IARC/IACR Tools for Cancer Registries" software  [16]. We applied international rules for multiple primary cancers (ICD-O) to eliminate duplicates and assigned the corresponding ICD-10 codes to all cases.
About 1. 27 million cancer cases for the whole period and 0. 57 million for the most recent period, 2008 -2017, were used in the analysis. All ages and ICD-10 diagnoses C00-96 were included in the analysis. In the graphs, we used data from 1993, and in the main analysis, the most recent periods, 2008 -2017. We extracted mid-year population estimates and cancer mortality rates by cause, sex, 5-year age group, and region from the Russian Fertility and Mortality Database (RFMD). RFMD is identical to the national statistics data collected by the Russian Federal State Statistics Service (RSSS)  [17]. We calculated age-standardized rates (ASRs) per 100,000 per calendar year using the Segi-Doll 1960 world standard population  [18]. We used these datasets previously to assess data comparability and validity  [5].
Historic data methods We used cases registered between 1993 and 2017 to assess the stability of incidence rates over time. To perform a preliminary assessment, we produced the plots for the number of cases (C00-C96) and age-standardized incidence rates (ASRs) per 100,000 per calendar year and ASRs per 100,000 for haematological malignancies. Then, we compared cancer incidence rates for age groups 0-4, 5-9 and 10-14 with the corresponding reference intervals based on deciles for childhood cancer published in IARC "Cancer Incidence in Five Continents (CI5)" volume XI  [19]. Finally, we examined the shape of age-specific curves (2008-2017)) on the arithmetic and semi-log scale against the curves from several European cancer registries from "Cancer Incidence in Five Continents" volume XI (2008-2012): (Bulgaria, Czech Republic, Latvia, Lithuania, Estonia and Poland (Cracow, Lower Silesia, Kielce, Podkarpackie).
Quantitative methods We applied the death certificate method to calculate completeness as the proportion of cases registered and the sum of registered and unregistered cases. Two sources are available in PBCR databases -"official cancer case notification" and "death certificate notification". Therefore, we used the following assumptions to distinguish between the two: 1) if the information on the date and cause of death was available, we considered the case as having information from the death certificate, 2) if the date of diagnosis was not equal to the date of death, we considered the case as having clinical notification, 3) if the date of diagnosis was equal to the date of death, and additional clinical information such as stage or treatment was present, we considered the case as having clinical notification.
Timeliness Timeliness is another data quality dimension linked to completeness. After the initial annual report is published, properly functioning cancer registries update the databases  [6]. Russia publishes the national cancer report that includes regional data in the late autumn of the following year, but aggregated data are being collected in January  [2]. Aggregated data is collected using special forms that include the number of cases by age (18 age groups) and gender for the ICD-10 cancer diagnoses and some additional information. It is considered too early for most cancer registries that aim at providing annual reports in one to three years  [3]. To assess the effect of early reporting, we compared the absolute number of cases from Northwestern Russian regions in 2008-2017 available in the ten annual national reports to the number of cases in our database extracted in December 2019.
The comparison of the annual report and registry database between 2008-2017 showed that about 10% of cases (or 23,304 cases) were not included in the initial national report for St. Petersburg. On the other hand, there were 19% fewer cases in the Leningrad oblast registry (Table  3 ). The differences for Arkhangelsk oblast, Murmansk oblast, Novgorod oblast, Republic of Karelia, and Vologda oblast were below 3%. Early reporting similarly affected most cancer types in the other eight regions, except liver and haematological malignancies (initial number overestimated) (Supplementary Table  S5 ). The number of cases for most cancer types was underestimated in the national report for St. Petersburg, and (Supplementary Table  S6 ), on the other hand, was lower in the Leningrad oblast registry database than in the national report.
Discussion Our study is the first comprehensive assessment of completeness in ten PBCRs of Northwestern regions of Russia. This study complements the assessment of comparability and validity published earlier  [5]  and provides the basis for using PBCRs datasets in epidemiological studies. Completeness is an essential criterion for PBCRs data quality, and it was above 90% in most regions (except St. Petersburg and Leningrad oblast). Relatively low completeness in St. Petersburg and Leningrad oblast is likely due to the size of the population and direct access to multiple cancer diagnostics and care facilities that do not notify PBCRs. Another important finding is the 10% difference in the number of cases in the PBCR database and the national report for St. Petersburg compared to other regions.
Difference between the M:I ratios and the survival did not exceed the predefined limit, indicating reasonable completeness for most cancer types in all the regions except Leningrad oblast and Vologda oblast. Leningrad oblast PBCR database was incomplete during 2014-2016, which was evident in the visual assessment of ASRs trends. Technical difficulties (software transition) and lack of resources (and staff ) may have caused the Leningrad oblast's data loss. Nevertheless, we included this database in our analyses to evaluate how different approaches to estimating completeness were performing in such a setting. A quantitative completeness assessment showed that completeness was above the reasonable limit of 90% in eight regions out of ten. Lincoln-Petersen estimator provided a realistic estimate of completeness in the Leningrad oblast, and both methods suggested a lack of completeness in St.
Petersburg. It is worth mentioning that the Lincoln-Petersen estimator provided a much more realistic estimate of completeness in the Leningrad oblast. Nevertheless, both methods provided similar results in St. Petersburg, suggesting that the assumptions for those methods probably hold. The Ajiki formula uses the M:I ratio, where mortality estimates are based on the independent source, the death registration system, while  the Lincoln-Petersen estimator relies on the number of cases and deaths registered at PBCR. Some differences in estimators arise from the quality of mortality linkage in PBCR. At the same time, in case of database losses (like in Leningrad oblast), the M:I ratio becomes unrealistically high, limiting the use of this formula for completeness assessment. Finally, despite early reporting, our timeliness assessment showed that the national annual report provided more complete statistics for smaller regions.
First, it is unclear if all DCI cases were accounted for in the database, and second, low DCO proportion (which is a part of DCI) may result from inefficient linking with the death certificate database. Finally, an autopsy practice (e. g. following more than 60% of the deaths in the Arkhangelsk oblast  [5] ) could be one of the reasons for the inflated DCO proportion. Despite all these limitations, the analysis presented in our paper utilised most of the semi-quantitative and quantitative methods used for completeness assessment. We could not apply the flow method  [4]  since the registration date was unavailable in our database during our analysis. Still, as far as we know, it is available in some PBCRs and can also be used for completeness assessments. In conclusion, more than half of the regional population-based cancer registries of Northwest Russia collect data with an acceptable degree of completeness (above 90%).