13 datasets found
  1. d

    SAS Programs - Claims-Based Frailty Index

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Sep 25, 2024
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    Kim, Dae Hyun; Gautam, Nileesa (2024). SAS Programs - Claims-Based Frailty Index [Dataset]. http://doi.org/10.7910/DVN/HM8DOI
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    Dataset updated
    Sep 25, 2024
    Dataset provided by
    Harvard Dataverse
    Authors
    Kim, Dae Hyun; Gautam, Nileesa
    Description

    This SAS program calculates CFI for each patient from analytic data files containing information on patient identifiers, ICD-9-CM diagnosis codes (version 32), ICD-10-CM Diagnosis Codes (version 2020), CPT codes, and HCPCS codes. NOTE: When downloading, store "CFI_ICD9CM_V32.tab", "CFI_ICD10CM_V2020.tab", and "PX_CODES.tab" as csv files (these files are originally stored as csv files, but Dataverse automatically converts them to tab files). Please read "Frailty-Index-SAS-code-Guide" before proceeding. Interpretation, validation data, and annotated references are provided in "Research Background - Claims-Based Frailty Index".

  2. D

    Data Quality Software and Solutions Report

    • marketresearchforecast.com
    doc, pdf, ppt
    Updated Mar 16, 2025
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    Market Research Forecast (2025). Data Quality Software and Solutions Report [Dataset]. https://www.marketresearchforecast.com/reports/data-quality-software-and-solutions-36352
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    doc, ppt, pdfAvailable download formats
    Dataset updated
    Mar 16, 2025
    Dataset authored and provided by
    Market Research Forecast
    License

    https://www.marketresearchforecast.com/privacy-policyhttps://www.marketresearchforecast.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The Data Quality Software and Solutions market is experiencing robust growth, driven by the increasing volume and complexity of data generated by businesses across all sectors. The market's expansion is fueled by a rising demand for accurate, consistent, and reliable data for informed decision-making, improved operational efficiency, and regulatory compliance. Key drivers include the surge in big data adoption, the growing need for data integration and governance, and the increasing prevalence of cloud-based solutions offering scalable and cost-effective data quality management capabilities. Furthermore, the rising adoption of advanced analytics and artificial intelligence (AI) is enhancing data quality capabilities, leading to more sophisticated solutions that can automate data cleansing, validation, and profiling processes. We estimate the 2025 market size to be around $12 billion, growing at a compound annual growth rate (CAGR) of 10% over the forecast period (2025-2033). This growth trajectory is being influenced by the rapid digital transformation across industries, necessitating higher data quality standards. Segmentation reveals a strong preference for cloud-based solutions due to their flexibility and scalability, with large enterprises driving a significant portion of the market demand. However, market growth faces some restraints. High implementation costs associated with data quality software and solutions, particularly for large-scale deployments, can be a barrier to entry for some businesses, especially SMEs. Also, the complexity of integrating these solutions with existing IT infrastructure can present challenges. The lack of skilled professionals proficient in data quality management is another factor impacting market growth. Despite these challenges, the market is expected to maintain a healthy growth trajectory, driven by increasing awareness of the value of high-quality data, coupled with the availability of innovative and user-friendly solutions. The competitive landscape is characterized by established players such as Informatica, IBM, and SAP, along with emerging players offering specialized solutions, resulting in a diverse range of options for businesses. Regional analysis indicates that North America and Europe currently hold significant market shares, but the Asia-Pacific region is projected to witness substantial growth in the coming years due to rapid digitalization and increasing data volumes.

  3. H

    Data for "Social Axioms on high school students in the North African...

    • dataverse.harvard.edu
    • produccioncientifica.ugr.es
    Updated Oct 31, 2020
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    Manuel García-Alonso (2020). Data for "Social Axioms on high school students in the North African context: Validation and fit of the SAS-II" [Dataset]. http://doi.org/10.7910/DVN/WAL0HO
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 31, 2020
    Dataset provided by
    Harvard Dataverse
    Authors
    Manuel García-Alonso
    License

    https://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.1/customlicense?persistentId=doi:10.7910/DVN/WAL0HOhttps://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.1/customlicense?persistentId=doi:10.7910/DVN/WAL0HO

    Area covered
    North Africa
    Description

    This dataverse was used to study the the validity of the Social Axioms Survey II (SAS-II) short form, Spanish version, in Melilla as a North Africa´s context.

  4. D

    Data Quality Management Service Report

    • marketresearchforecast.com
    doc, pdf, ppt
    Updated Apr 27, 2025
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    Market Research Forecast (2025). Data Quality Management Service Report [Dataset]. https://www.marketresearchforecast.com/reports/data-quality-management-service-538930
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    pdf, doc, pptAvailable download formats
    Dataset updated
    Apr 27, 2025
    Dataset authored and provided by
    Market Research Forecast
    License

    https://www.marketresearchforecast.com/privacy-policyhttps://www.marketresearchforecast.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The Data Quality Management (DQM) services market is experiencing robust growth, driven by the increasing volume and complexity of data generated by businesses across diverse sectors. The market's expansion is fueled by several key factors. Firstly, the burgeoning adoption of cloud-based solutions offers scalability and cost-effectiveness, making DQM accessible to even SMEs. Secondly, stringent data regulations like GDPR and CCPA are compelling organizations to prioritize data accuracy and compliance, significantly boosting demand for DQM services. Thirdly, the rise of big data analytics and AI initiatives necessitate high-quality data as a foundation, further driving market growth. Finally, the strategic shift towards data-driven decision-making necessitates accurate, reliable data, increasing reliance on DQM solutions. While the on-premises segment currently holds a significant market share, the cloud-based segment is expected to witness accelerated growth due to its flexibility and ease of deployment. Large enterprises, with their substantial data volumes and complex data landscapes, currently dominate the application segment. However, growing awareness among SMEs about the benefits of data quality and improving affordability of DQM solutions are expanding this segment's market share rapidly. Competitive dynamics are characterized by a mix of established players like IBM, Informatica, and SAS Institute, alongside emerging niche players offering specialized solutions. Geographical distribution shows North America and Europe currently dominating the market, but the Asia-Pacific region is predicted to experience the fastest growth rate over the forecast period due to increased digitalization and government initiatives supporting data infrastructure development. Market restraints include the high initial investment costs associated with implementing DQM solutions, the complexity of integrating these solutions with existing systems, and the shortage of skilled professionals proficient in data quality management. Despite these challenges, the long-term outlook for the DQM services market remains exceptionally positive, projected to maintain a healthy CAGR through 2033.

  5. d

    Replication Data for: Data base of Validation and Analysis of the metric...

    • dataone.org
    • search.dataone.org
    Updated Mar 6, 2024
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    Livacic-Rojas, Pablo (2024). Replication Data for: Data base of Validation and Analysis of the metric Properties of the Leadership Questionnaire [Dataset]. http://doi.org/10.7910/DVN/AEX8PP
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    Dataset updated
    Mar 6, 2024
    Dataset provided by
    Harvard Dataverse
    Authors
    Livacic-Rojas, Pablo
    Description

    The leadership and personal competencies exhibits limitations in terms of construct definition, behavior specifications and valid theory-based measuring strategies. An explanatory design with latent variables and the statistical software SAS 9.4 were used for the validation and adaptation to Spanish of the Leadership Virtues Questionnaire applied to work and organizational psychologists and people who exercise leadership functions in Chile. The levels of agreement between judges for the adaptation to the Spanish language and the confirmatory factor analysis of first order with four dimensions shows insufficient statistical indices for the absolute, comparative and parsimonious adjustments. However, a second-order confirmatory factor analysis with two dimensions presents a satisfactory fit for the item, model, and parameter matrices. The measurement of Virtuous Leadership would provide relevant inputs for further evaluation and training based on ethical competencies aimed at improving management, which would, in turn, allow for its treatment as an independent variable to generate an ethical organizational culture.

  6. d

    Data from: Developing and validating the psychosocial burden among people...

    • datadryad.org
    zip
    Updated Nov 18, 2020
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    M. Antonia Biggs; Torsten Neilands; Shelly Kaller; Erin Wingo; Lauren Ralph (2020). Developing and validating the psychosocial burden among people seeking abortion scale (PB-SAS) [Dataset]. http://doi.org/10.7272/Q6X63K6C
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    zipAvailable download formats
    Dataset updated
    Nov 18, 2020
    Dataset provided by
    Dryad
    Authors
    M. Antonia Biggs; Torsten Neilands; Shelly Kaller; Erin Wingo; Lauren Ralph
    Time period covered
    2020
    Description

    We recruited study participants from four abortion facilities located in three U.S. states. To be eligible, people had to be seeking an abortion at the time of recruitment, aged 15 years or older, and able to speak and read English or Spanish. Research staff introduced the study to patients while they were waiting for their appointment, handed interested patients a tablet device to complete and confirm their eligibility, and consented those eligible and interested to participate in the study. Participants self-administered an anonymous survey which they could choose to complete in either English or Spanish, with research staff available to assist as needed.

  7. ANES 1986 Time Series Study - Archival Version

    • search.gesis.org
    Updated Nov 10, 2015
    + more versions
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    GESIS search (2015). ANES 1986 Time Series Study - Archival Version [Dataset]. http://doi.org/10.3886/ICPSR08678
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    Dataset updated
    Nov 10, 2015
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    GESIS search
    License

    https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de443631https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de443631

    Description

    Abstract (en): This study is part of a time-series collection of national surveys fielded continuously since 1952. The election studies are designed to present data on Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life. In addition to core items, new content includes questions on values, political knowledge, and attitudes on racial policy, as well as more general attitudes conceptualized as antecedent to these opinions on racial issues. The Main Data File also contains vote validation data that were expanded to include information from the appropriate election office and were attached to the records of each of the respondents in the post-election survey. The expanded data consist of the respondent's post case ID, vote validation ID, and two variables to clarify the distinction between the office of registration and the office associated with the respondent's sample address. The second data file, Bias Nonresponse Data File, contains respondent-level field administration variables. Of 3,833 lines of sample that were originally issued for the 1990 Study, 2,176 resulted in completed interviews, others were nonsample, and others were noninterviews for a variety of reasons. For each line of sample, the Bias Nonresponse Data File includes sampling data, result codes, control variables, and interviewer variables. Detailed geocode data are blanked but available under conditions of confidential access (contact the American National Election Studies at the Center for Political Studies, University of Michigan, for further details). This is a specialized file, of particular interest to those who are interested in survey nonresponse. Demographic variables include age, party affiliation, marital status, education, employment status, occupation, religious preference, and ethnicity. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. Response Rates: The response rate for this study is 67.7 percent. The study was in the field until January 31, although 67 percent of the interviews were taken by November 25, 80 percent by December 7, and 93 percent by December 31. All United States households in the 50 states. National multistage area probability sample. 2015-11-10 The study metadata was updated.2009-01-09 YYYY-MM-DD Part 1, the Main Data File, incorporates errata that were posted separately under the Fourth ICPSR Edition. Part 2, the Bias Nonresponse Data File, has been added to the data collection, along with corresponding SAS, SPSS, and Stata setup files and documentation. The codebook has been updated by adding a technical memorandum on the sampling design of the study previously missing from the codebook. The nonresponse file contains respondent-level field administration variables for those interested in survey nonresponse. The collection now includes files in ASCII, SPSS portable, SAS transport (CPORT), and Stata system formats.2000-02-21 The data for this study are now available in SAS transport and SPSS export formats in addition to the ASCII data file. Variables in the dataset have been renumbered to the following format: 2-digit (or 2-character) year prefix + 4 digits + [optional] 1-character suffix. Dataset ID and version variables have also been added. Additionally, the Voter Validation Office Administration Interview File (Expanded Version) has been merged with the main data file, and the codebook and SPSS setup files have been replaced. Also, SAS setup files have been added to the collection, and the data collection instrument is now provided as a PDF file. Two files are no longer being released with this collection: the Voter Validation Office Administration Interview File (Unexpanded Version) and the Results of First Contact With Respondent file. Funding insitution(s): National Science Foundation (SOC77-08885 and SES-8341310). face-to-face interviewThere was significantly more content in this post-election survey than ...

  8. o

    Data LVQ

    • openicpsr.org
    Updated Nov 8, 2023
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    Pablo Livacic (2023). Data LVQ [Dataset]. http://doi.org/10.3886/E195003V3
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    Dataset updated
    Nov 8, 2023
    Dataset provided by
    University of Santiago of Chile
    Authors
    Pablo Livacic
    License

    Attribution-NoDerivs 4.0 (CC BY-ND 4.0)https://creativecommons.org/licenses/by-nd/4.0/
    License information was derived automatically

    Time period covered
    Aug 17, 2021 - Jun 22, 2022
    Area covered
    Chile
    Description

    The literature on leadership and one's own competencies shows limitations in terms of the definition of the construct, specifications of behaviors and its measurement associated with a diversity of theories without sufficient evidence. An explanatory design with latent variables and the statistical software SAS 9.4 were used for the validation and adaptation to Spanish and analysis of the data of the items, dimensions, fit of the model in the Virtuous Leadership Questionnaire in work psychologists and organizations and people who exercise leadership functions in Chile. The levels of agreement between judges for the adaptation to the Spanish language, the confirmatory factorial analysis of first order with four dimensions shows statistical indices of inefficient for the absolute, comparative and parsimonious adjustments. However, second-order confirmatory factor analysis with two dimensions shows an efficient fit for the item, model, and parameter matrices. Virtuous leadership would be feasible to measure and would provide relevant inputs for subsequent evaluation and training through ethical competencies aimed at improving management, which would allow its treatment as an independent variable to generate an ethical organizational culture.

  9. w

    Global Data Cleansing Tool Market Research Report: By Application (Data...

    • wiseguyreports.com
    Updated Jan 5, 2025
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    wWiseguy Research Consultants Pvt Ltd (2025). Global Data Cleansing Tool Market Research Report: By Application (Data Quality Management, Data Migration, Data Integration, Customer Data Management), By Deployment Type (On-Premises, Cloud-Based, Hybrid), By End User (BFSI, Healthcare, Retail, Manufacturing, Telecommunications), By Features (Data Profiling, Data Matching, Data Validation, Data Enrichment) and By Regional (North America, Europe, South America, Asia Pacific, Middle East and Africa) - Forecast to 2032. [Dataset]. https://www.wiseguyreports.com/de/reports/data-cleansing-tool-market
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    Dataset updated
    Jan 5, 2025
    Dataset authored and provided by
    wWiseguy Research Consultants Pvt Ltd
    License

    https://www.wiseguyreports.com/pages/privacy-policyhttps://www.wiseguyreports.com/pages/privacy-policy

    Area covered
    Global
    Description
    BASE YEAR2024
    HISTORICAL DATA2019 - 2024
    REPORT COVERAGERevenue Forecast, Competitive Landscape, Growth Factors, and Trends
    MARKET SIZE 20232.67(USD Billion)
    MARKET SIZE 20242.95(USD Billion)
    MARKET SIZE 20326.5(USD Billion)
    SEGMENTS COVEREDApplication, Deployment Type, End User, Features, Regional
    COUNTRIES COVEREDNorth America, Europe, APAC, South America, MEA
    KEY MARKET DYNAMICSdata quality improvement, regulatory compliance demand, cloud integration growth, advanced analytics adoption, increasing data volumes
    MARKET FORECAST UNITSUSD Billion
    KEY COMPANIES PROFILEDTrifacta, Melissa Data, Pitney Bowes, Microsoft, IBM, Dun and Bradstreet, Experian, Talend, Oracle, TIBCO Software, Informatica, Data Ladder, Precisely, SAP, SAS
    MARKET FORECAST PERIOD2025 - 2032
    KEY MARKET OPPORTUNITIESAI-driven automation integration, Rising demand for data quality, Increased regulatory compliance requirements, Expansion in e-commerce sectors, Growing adoption of cloud solutions
    COMPOUND ANNUAL GROWTH RATE (CAGR) 10.38% (2025 - 2032)
  10. Comparing the Shenoy et al [21] algorithm for low-value urinalysis and...

    • plos.figshare.com
    xls
    Updated Jun 7, 2023
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    Kelsey Chalmers; Valérie Gopinath; Adam G. Elshaug (2023). Comparing the Shenoy et al [21] algorithm for low-value urinalysis and important diagnosis codes in the HSR Definition Builder application. [Dataset]. http://doi.org/10.1371/journal.pone.0266154.t004
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    xlsAvailable download formats
    Dataset updated
    Jun 7, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Kelsey Chalmers; Valérie Gopinath; Adam G. Elshaug
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Comparing the Shenoy et al [21] algorithm for low-value urinalysis and important diagnosis codes in the HSR Definition Builder application.

  11. f

    The selected example codes and their definitions.

    • plos.figshare.com
    xls
    Updated Jun 7, 2023
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    Kelsey Chalmers; Valérie Gopinath; Adam G. Elshaug (2023). The selected example codes and their definitions. [Dataset]. http://doi.org/10.1371/journal.pone.0266154.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 7, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Kelsey Chalmers; Valérie Gopinath; Adam G. Elshaug
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The selected example codes and their definitions.

  12. f

    Top 21 of 132 diagnosis codes for carrier claims with a knee arthroscopy...

    • plos.figshare.com
    xls
    Updated Jun 7, 2023
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    Kelsey Chalmers; Valérie Gopinath; Adam G. Elshaug (2023). Top 21 of 132 diagnosis codes for carrier claims with a knee arthroscopy procedure (CPT 29877), ordered by relative importance from the classification model. [Dataset]. http://doi.org/10.1371/journal.pone.0266154.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 7, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Kelsey Chalmers; Valérie Gopinath; Adam G. Elshaug
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Top 21 of 132 diagnosis codes for carrier claims with a knee arthroscopy procedure (CPT 29877), ordered by relative importance from the classification model.

  13. e

    Table non géométrique à utiliser pour décrire les zones multi-aléas du PPRT...

    • data.europa.eu
    • demo.georchestra.org
    esri shape, json, zip
    Updated Feb 17, 2015
    + more versions
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    DDTM Pas-de-Calais (2015). Table non géométrique à utiliser pour décrire les zones multi-aléas du PPRT de Vynova Mazingarbe SAS et Maxam Tan (ex SAV Grande Paroisse) [Dataset]. https://data.europa.eu/data/datasets/58aeefccc751df58a14c3ee8?locale=hu
    Explore at:
    json, zip, esri shapeAvailable download formats
    Dataset updated
    Feb 17, 2015
    Dataset authored and provided by
    DDTM Pas-de-Calais
    License

    https://www.etalab.gouv.fr/licence-ouverte-open-licencehttps://www.etalab.gouv.fr/licence-ouverte-open-licence

    Area covered
    Mazingarbe
    Description

    Table non géométrique à utiliser pour décrire les zones multi-aléas dans les cas de PPRT multirisques. Cette table complémentaire permet de renseigner toutes les informations relatives à une zone multi-aléas : renseigner tous les types d'aléa auxquels elle est exposée, renseigner le niveau à chaque aléa (les zones exposées à plusieurs aléas comportent autant de niveaux que de types d'aléa recensés).

    Origine

    GÉNÉALOGIE : Au départ digitalisation au 1/50 000 par le MNHN.

    Sur les 9 RNV, 7 ont été précisées à partir du scan 25, 1 par le CSL (cadastre pour l''ex-APB de la tourbière de Charmes dans les Vosges) . Exhaustivité : bonne.

    Il restait à effectuer a validation nationale par le MNHN de la RNV des tourbières et landes du pays de Bitche mais la donnée a été remplacés par les réserves naturelles régional

    Le lot de données faisait l''objet d''une validation par le MNHN.

    QUALITÉ DES DONNÉES : Les objets ont été digitalisés sur SCAN 25 IGN.

    Organisations partenaires

    DDTM Pas-de-Calais

    Liens annexes

    Consulter cette fiche sur geo.data.gouv.fr

  14. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Kim, Dae Hyun; Gautam, Nileesa (2024). SAS Programs - Claims-Based Frailty Index [Dataset]. http://doi.org/10.7910/DVN/HM8DOI

SAS Programs - Claims-Based Frailty Index

Explore at:
12 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Sep 25, 2024
Dataset provided by
Harvard Dataverse
Authors
Kim, Dae Hyun; Gautam, Nileesa
Description

This SAS program calculates CFI for each patient from analytic data files containing information on patient identifiers, ICD-9-CM diagnosis codes (version 32), ICD-10-CM Diagnosis Codes (version 2020), CPT codes, and HCPCS codes. NOTE: When downloading, store "CFI_ICD9CM_V32.tab", "CFI_ICD10CM_V2020.tab", and "PX_CODES.tab" as csv files (these files are originally stored as csv files, but Dataverse automatically converts them to tab files). Please read "Frailty-Index-SAS-code-Guide" before proceeding. Interpretation, validation data, and annotated references are provided in "Research Background - Claims-Based Frailty Index".

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