Automated recruitment and randomisation for an efficient randomised controlled trial in primary care View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Victoria R. Cornelius, Lisa McDermott, Alice S. Forster, Mark Ashworth, Alison J. Wright, Martin C. Gulliford

ABSTRACT

BACKGROUND/AIMS: Use of electronic health records and information technology to deliver more efficient clinical trials is attracting the attention of research funders and researchers. We report on methodological issues and data quality for a comparison of 'automated' and manual (or 'in-practice') methods for recruitment and randomisation in a large randomised controlled trial, with individual patient allocation in primary care. METHODS: We conducted a three-arm randomised controlled trial in primary care to evaluate interventions to improve the uptake of invited NHS health checks for cardiovascular risk assessment. Eligible participants were identified using a borough-wide health check management information system. An in-practice recruitment and randomisation method used at 12 general practices required the research team to complete monthly visits to each general practice. For the fully automated method, employed for six general practices, randomisation of eligible participants was performed automatically and remotely using a bespoke algorithm embedded in the health check management information system. RESULTS: There were 8588 and 4093 participants recruited for the manual and automated methods, respectively. The in-practice method was ready for implementation 3 months sooner than the automated method and the in-practice method allowed for full control and documentation of the randomisation procedure. However the in-practice approach was labour intensive and the requirement for participant records to be stored locally resulted in the loss of data for 10 practice months. No records for participants allocated using the automated method were lost. A fixed-effects meta-analysis showed that effect estimates for the primary outcome were consistent for the two allocation methods. CONCLUSIONS: This trial demonstrated the feasibility of automated recruitment and randomisation methods into a randomised controlled trial performed in primary care. Future research should explore the application of these techniques in other clinical contexts and health care settings. TRIAL REGISTRATION: Current Controlled Trials, ID: ISRCTN42856343 . Registered on 21 March 2013. More... »

PAGES

341

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13063-018-2723-3

DOI

http://dx.doi.org/10.1186/s13063-018-2723-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1105151368

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29945656


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1117", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Public Health and Health Services", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Automation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Data Accuracy", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Data Mining", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Diabetes Mellitus", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Electronic Health Records", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Health Services Research", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Heart Diseases", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "London", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Patient Selection", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Primary Health Care", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Renal Insufficiency, Chronic", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Risk Factors", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "State Medicine", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Stroke", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Imperial College London", 
          "id": "https://www.grid.ac/institutes/grid.7445.2", 
          "name": [
            "Department of Primary Care and Public Health Sciences, King\u2019s College, London, UK", 
            "Imperial Clinical Trials Unit, Imperial College London, 68 Wood Lane, W12 7RH, London, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cornelius", 
        "givenName": "Victoria R.", 
        "id": "sg:person.01205756377.89", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01205756377.89"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "King's College London", 
          "id": "https://www.grid.ac/institutes/grid.13097.3c", 
          "name": [
            "Department of Primary Care and Public Health Sciences, King\u2019s College, London, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "McDermott", 
        "givenName": "Lisa", 
        "id": "sg:person.01037227042.72", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01037227042.72"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University College London", 
          "id": "https://www.grid.ac/institutes/grid.83440.3b", 
          "name": [
            "Department of Primary Care and Public Health Sciences, King\u2019s College, London, UK", 
            "Department of Behavioural Science and Health, University College, London, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Forster", 
        "givenName": "Alice S.", 
        "id": "sg:person.011207662455.39", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011207662455.39"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "King's College London", 
          "id": "https://www.grid.ac/institutes/grid.13097.3c", 
          "name": [
            "Department of Primary Care and Public Health Sciences, King\u2019s College, London, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ashworth", 
        "givenName": "Mark", 
        "id": "sg:person.01125543116.56", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01125543116.56"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "St Thomas' Hospital", 
          "id": "https://www.grid.ac/institutes/grid.425213.3", 
          "name": [
            "Department of Primary Care and Public Health Sciences, King\u2019s College, London, UK", 
            "NIHR Biomedical Research Centre at Guy\u2019s and St Thomas\u2019 Hospital, London, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wright", 
        "givenName": "Alison J.", 
        "id": "sg:person.016022244512.49", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016022244512.49"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "St Thomas' Hospital", 
          "id": "https://www.grid.ac/institutes/grid.425213.3", 
          "name": [
            "Department of Primary Care and Public Health Sciences, King\u2019s College, London, UK", 
            "NIHR Biomedical Research Centre at Guy\u2019s and St Thomas\u2019 Hospital, London, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gulliford", 
        "givenName": "Martin C.", 
        "id": "sg:person.0723000442.22", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0723000442.22"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1161/strokeaha.114.005713", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003515016"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/strokeaha.114.005713", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003515016"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/strokeaha.114.005713", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003515016"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/bmjopen-2015-010892", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011283393"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1745-6215-15-342", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011626466", 
          "https://doi.org/10.1186/1745-6215-15-342"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1370/afm.1659", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018844804"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1745-6215-14-297", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020413696", 
          "https://doi.org/10.1186/1745-6215-14-297"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejmoa1608033", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021800058"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/1740774511398368", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024317131"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/1740774511398368", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024317131"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/bmj.e55", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029700767"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s13063-015-0776-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030912277", 
          "https://doi.org/10.1186/s13063-015-0776-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s13063-015-0776-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030912277", 
          "https://doi.org/10.1186/s13063-015-0776-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/fampra/cmt002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044819600"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/sim.1186", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047418980"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/bmjopen-2015-010069", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047879522"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3310/hta18430", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071139425"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3310/hta20840", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071139640"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/1740774510395635", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1078379851"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s13063-017-2394-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1100316374", 
          "https://doi.org/10.1186/s13063-017-2394-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s13063-017-2394-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1100316374", 
          "https://doi.org/10.1186/s13063-017-2394-5"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-12", 
    "datePublishedReg": "2018-12-01", 
    "description": "BACKGROUND/AIMS: Use of electronic health records and information technology to deliver more efficient clinical trials is attracting the attention of research funders and researchers. We report on methodological issues and data quality for a comparison of 'automated' and manual (or 'in-practice') methods for recruitment and randomisation in a large randomised controlled trial, with individual patient allocation in primary care.\nMETHODS: We conducted a three-arm randomised controlled trial in primary care to evaluate interventions to improve the uptake of invited NHS health checks for cardiovascular risk assessment. Eligible participants were identified using a borough-wide health check management information system. An in-practice recruitment and randomisation method used at 12 general practices required the research team to complete monthly visits to each general practice. For the fully automated method, employed for six general practices, randomisation of eligible participants was performed automatically and remotely using a bespoke algorithm embedded in the health check management information system.\nRESULTS: There were 8588 and 4093 participants recruited for the manual and automated methods, respectively. The in-practice method was ready for implementation 3 months sooner than the automated method and the in-practice method allowed for full control and documentation of the randomisation procedure. However the in-practice approach was labour intensive and the requirement for participant records to be stored locally resulted in the loss of data for 10 practice months. No records for participants allocated using the automated method were lost. A fixed-effects meta-analysis showed that effect estimates for the primary outcome were consistent for the two allocation methods.\nCONCLUSIONS: This trial demonstrated the feasibility of automated recruitment and randomisation methods into a randomised controlled trial performed in primary care. Future research should explore the application of these techniques in other clinical contexts and health care settings.\nTRIAL REGISTRATION: Current Controlled Trials, ID: ISRCTN42856343 . Registered on 21 March 2013.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/s13063-018-2723-3", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.5151923", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1297400", 
        "issn": [
          "1468-6708", 
          "1745-6215"
        ], 
        "name": "Trials", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "19"
      }
    ], 
    "name": "Automated recruitment and randomisation for an efficient randomised controlled trial in primary care", 
    "pagination": "341", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "cb692ced275d3af523919383ec3d4c95d09949054849a76a578a9993b2061c6e"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "29945656"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101263253"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s13063-018-2723-3"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1105151368"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s13063-018-2723-3", 
      "https://app.dimensions.ai/details/publication/pub.1105151368"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T11:48", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000358_0000000358/records_127463_00000011.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1186%2Fs13063-018-2723-3"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s13063-018-2723-3'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s13063-018-2723-3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s13063-018-2723-3'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s13063-018-2723-3'


 

This table displays all metadata directly associated to this object as RDF triples.

234 TRIPLES      21 PREDICATES      61 URIs      37 LITERALS      25 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s13063-018-2723-3 schema:about N04afb99d3cdd4433b7fef1ca111d1b22
2 N15aa66b8d87e4385bebf537f30b908fd
3 N2bac35c5fde542929c19fbd48d407da9
4 N720c42a255f04ff3a4be3b56d3b26a56
5 N7d9d2cb8fe284238b133c6ae1c03a4ad
6 N9961a30a0c744ae29a01e42d44e01b7d
7 N99bcfae75ed440ada2712015874968ae
8 Na34a1d1887cf4b0d86824818264d76d9
9 Nadc5dba871fe4a9986cb9daa045b419d
10 Nafddcc3f46fc4334adbaf3a7c5618eb7
11 Nb25cef92f07f42678958377332251eb2
12 Nc12b9b72f4434a44b41a6a43dea9dc44
13 Nc3e443b013914a7784a1ac67adbc2da9
14 Nccb9208eff2f46c5ab8b7674ec270004
15 Ne0c54a55f614444dab84131af9913c4e
16 Nf3dd0739fb7a4f738ade299c8d89eca7
17 anzsrc-for:11
18 anzsrc-for:1117
19 schema:author Nb7b7be4fd100430485f4aaf213265e91
20 schema:citation sg:pub.10.1186/1745-6215-14-297
21 sg:pub.10.1186/1745-6215-15-342
22 sg:pub.10.1186/s13063-015-0776-0
23 sg:pub.10.1186/s13063-017-2394-5
24 https://doi.org/10.1002/sim.1186
25 https://doi.org/10.1056/nejmoa1608033
26 https://doi.org/10.1093/fampra/cmt002
27 https://doi.org/10.1136/bmj.e55
28 https://doi.org/10.1136/bmjopen-2015-010069
29 https://doi.org/10.1136/bmjopen-2015-010892
30 https://doi.org/10.1161/strokeaha.114.005713
31 https://doi.org/10.1177/1740774510395635
32 https://doi.org/10.1177/1740774511398368
33 https://doi.org/10.1370/afm.1659
34 https://doi.org/10.3310/hta18430
35 https://doi.org/10.3310/hta20840
36 schema:datePublished 2018-12
37 schema:datePublishedReg 2018-12-01
38 schema:description BACKGROUND/AIMS: Use of electronic health records and information technology to deliver more efficient clinical trials is attracting the attention of research funders and researchers. We report on methodological issues and data quality for a comparison of 'automated' and manual (or 'in-practice') methods for recruitment and randomisation in a large randomised controlled trial, with individual patient allocation in primary care. METHODS: We conducted a three-arm randomised controlled trial in primary care to evaluate interventions to improve the uptake of invited NHS health checks for cardiovascular risk assessment. Eligible participants were identified using a borough-wide health check management information system. An in-practice recruitment and randomisation method used at 12 general practices required the research team to complete monthly visits to each general practice. For the fully automated method, employed for six general practices, randomisation of eligible participants was performed automatically and remotely using a bespoke algorithm embedded in the health check management information system. RESULTS: There were 8588 and 4093 participants recruited for the manual and automated methods, respectively. The in-practice method was ready for implementation 3 months sooner than the automated method and the in-practice method allowed for full control and documentation of the randomisation procedure. However the in-practice approach was labour intensive and the requirement for participant records to be stored locally resulted in the loss of data for 10 practice months. No records for participants allocated using the automated method were lost. A fixed-effects meta-analysis showed that effect estimates for the primary outcome were consistent for the two allocation methods. CONCLUSIONS: This trial demonstrated the feasibility of automated recruitment and randomisation methods into a randomised controlled trial performed in primary care. Future research should explore the application of these techniques in other clinical contexts and health care settings. TRIAL REGISTRATION: Current Controlled Trials, ID: ISRCTN42856343 . Registered on 21 March 2013.
39 schema:genre research_article
40 schema:inLanguage en
41 schema:isAccessibleForFree true
42 schema:isPartOf N1e4ac19a564347df80918b95f43714dd
43 N9c31d066b22f4cf4857f714a6561c6fe
44 sg:journal.1297400
45 schema:name Automated recruitment and randomisation for an efficient randomised controlled trial in primary care
46 schema:pagination 341
47 schema:productId N07c27314bd8b414490082fd21e7f5504
48 N5d2a4b5620b94752a6a021b0626a2dd3
49 N69d505fb79464f7a951349513e3c86b4
50 Nc229acf7097b4f829d14d3e93445833e
51 Ncba03357dba54a7ba3fed1813e5fab57
52 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105151368
53 https://doi.org/10.1186/s13063-018-2723-3
54 schema:sdDatePublished 2019-04-11T11:48
55 schema:sdLicense https://scigraph.springernature.com/explorer/license/
56 schema:sdPublisher N8335f13d648e4e068760c3b675161fcc
57 schema:url https://link.springer.com/10.1186%2Fs13063-018-2723-3
58 sgo:license sg:explorer/license/
59 sgo:sdDataset articles
60 rdf:type schema:ScholarlyArticle
61 N04afb99d3cdd4433b7fef1ca111d1b22 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
62 schema:name Humans
63 rdf:type schema:DefinedTerm
64 N07c27314bd8b414490082fd21e7f5504 schema:name doi
65 schema:value 10.1186/s13063-018-2723-3
66 rdf:type schema:PropertyValue
67 N15aa66b8d87e4385bebf537f30b908fd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
68 schema:name Primary Health Care
69 rdf:type schema:DefinedTerm
70 N1e4ac19a564347df80918b95f43714dd schema:volumeNumber 19
71 rdf:type schema:PublicationVolume
72 N2bac35c5fde542929c19fbd48d407da9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
73 schema:name Data Accuracy
74 rdf:type schema:DefinedTerm
75 N5d2a4b5620b94752a6a021b0626a2dd3 schema:name nlm_unique_id
76 schema:value 101263253
77 rdf:type schema:PropertyValue
78 N69d505fb79464f7a951349513e3c86b4 schema:name readcube_id
79 schema:value cb692ced275d3af523919383ec3d4c95d09949054849a76a578a9993b2061c6e
80 rdf:type schema:PropertyValue
81 N708fcf78174d4ad2a32247e2f7cf3191 rdf:first sg:person.011207662455.39
82 rdf:rest N7bbde4bb6d5544ec9fa39b42cdd62718
83 N720c42a255f04ff3a4be3b56d3b26a56 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
84 schema:name Automation
85 rdf:type schema:DefinedTerm
86 N7bbde4bb6d5544ec9fa39b42cdd62718 rdf:first sg:person.01125543116.56
87 rdf:rest Na4ece65afbf445ffa2ac88b74471da5e
88 N7d9d2cb8fe284238b133c6ae1c03a4ad schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
89 schema:name Electronic Health Records
90 rdf:type schema:DefinedTerm
91 N8335f13d648e4e068760c3b675161fcc schema:name Springer Nature - SN SciGraph project
92 rdf:type schema:Organization
93 N939b5397d25747fba1a0b72f5f24bbe0 rdf:first sg:person.01037227042.72
94 rdf:rest N708fcf78174d4ad2a32247e2f7cf3191
95 N9961a30a0c744ae29a01e42d44e01b7d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
96 schema:name London
97 rdf:type schema:DefinedTerm
98 N99bcfae75ed440ada2712015874968ae schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
99 schema:name Heart Diseases
100 rdf:type schema:DefinedTerm
101 N9c31d066b22f4cf4857f714a6561c6fe schema:issueNumber 1
102 rdf:type schema:PublicationIssue
103 Na34a1d1887cf4b0d86824818264d76d9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
104 schema:name Diabetes Mellitus
105 rdf:type schema:DefinedTerm
106 Na4ece65afbf445ffa2ac88b74471da5e rdf:first sg:person.016022244512.49
107 rdf:rest Nf7cc9fe6730e411892593f60c38cdc34
108 Nadc5dba871fe4a9986cb9daa045b419d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
109 schema:name Health Services Research
110 rdf:type schema:DefinedTerm
111 Nafddcc3f46fc4334adbaf3a7c5618eb7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
112 schema:name Algorithms
113 rdf:type schema:DefinedTerm
114 Nb25cef92f07f42678958377332251eb2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
115 schema:name Risk Factors
116 rdf:type schema:DefinedTerm
117 Nb7b7be4fd100430485f4aaf213265e91 rdf:first sg:person.01205756377.89
118 rdf:rest N939b5397d25747fba1a0b72f5f24bbe0
119 Nc12b9b72f4434a44b41a6a43dea9dc44 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
120 schema:name Stroke
121 rdf:type schema:DefinedTerm
122 Nc229acf7097b4f829d14d3e93445833e schema:name dimensions_id
123 schema:value pub.1105151368
124 rdf:type schema:PropertyValue
125 Nc3e443b013914a7784a1ac67adbc2da9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
126 schema:name Data Mining
127 rdf:type schema:DefinedTerm
128 Ncba03357dba54a7ba3fed1813e5fab57 schema:name pubmed_id
129 schema:value 29945656
130 rdf:type schema:PropertyValue
131 Nccb9208eff2f46c5ab8b7674ec270004 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
132 schema:name Patient Selection
133 rdf:type schema:DefinedTerm
134 Ne0c54a55f614444dab84131af9913c4e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
135 schema:name Renal Insufficiency, Chronic
136 rdf:type schema:DefinedTerm
137 Nf3dd0739fb7a4f738ade299c8d89eca7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
138 schema:name State Medicine
139 rdf:type schema:DefinedTerm
140 Nf7cc9fe6730e411892593f60c38cdc34 rdf:first sg:person.0723000442.22
141 rdf:rest rdf:nil
142 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
143 schema:name Medical and Health Sciences
144 rdf:type schema:DefinedTerm
145 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
146 schema:name Public Health and Health Services
147 rdf:type schema:DefinedTerm
148 sg:grant.5151923 http://pending.schema.org/fundedItem sg:pub.10.1186/s13063-018-2723-3
149 rdf:type schema:MonetaryGrant
150 sg:journal.1297400 schema:issn 1468-6708
151 1745-6215
152 schema:name Trials
153 rdf:type schema:Periodical
154 sg:person.01037227042.72 schema:affiliation https://www.grid.ac/institutes/grid.13097.3c
155 schema:familyName McDermott
156 schema:givenName Lisa
157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01037227042.72
158 rdf:type schema:Person
159 sg:person.011207662455.39 schema:affiliation https://www.grid.ac/institutes/grid.83440.3b
160 schema:familyName Forster
161 schema:givenName Alice S.
162 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011207662455.39
163 rdf:type schema:Person
164 sg:person.01125543116.56 schema:affiliation https://www.grid.ac/institutes/grid.13097.3c
165 schema:familyName Ashworth
166 schema:givenName Mark
167 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01125543116.56
168 rdf:type schema:Person
169 sg:person.01205756377.89 schema:affiliation https://www.grid.ac/institutes/grid.7445.2
170 schema:familyName Cornelius
171 schema:givenName Victoria R.
172 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01205756377.89
173 rdf:type schema:Person
174 sg:person.016022244512.49 schema:affiliation https://www.grid.ac/institutes/grid.425213.3
175 schema:familyName Wright
176 schema:givenName Alison J.
177 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016022244512.49
178 rdf:type schema:Person
179 sg:person.0723000442.22 schema:affiliation https://www.grid.ac/institutes/grid.425213.3
180 schema:familyName Gulliford
181 schema:givenName Martin C.
182 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0723000442.22
183 rdf:type schema:Person
184 sg:pub.10.1186/1745-6215-14-297 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020413696
185 https://doi.org/10.1186/1745-6215-14-297
186 rdf:type schema:CreativeWork
187 sg:pub.10.1186/1745-6215-15-342 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011626466
188 https://doi.org/10.1186/1745-6215-15-342
189 rdf:type schema:CreativeWork
190 sg:pub.10.1186/s13063-015-0776-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030912277
191 https://doi.org/10.1186/s13063-015-0776-0
192 rdf:type schema:CreativeWork
193 sg:pub.10.1186/s13063-017-2394-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100316374
194 https://doi.org/10.1186/s13063-017-2394-5
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1002/sim.1186 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047418980
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1056/nejmoa1608033 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021800058
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1093/fampra/cmt002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044819600
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1136/bmj.e55 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029700767
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1136/bmjopen-2015-010069 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047879522
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1136/bmjopen-2015-010892 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011283393
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1161/strokeaha.114.005713 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003515016
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1177/1740774510395635 schema:sameAs https://app.dimensions.ai/details/publication/pub.1078379851
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1177/1740774511398368 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024317131
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1370/afm.1659 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018844804
215 rdf:type schema:CreativeWork
216 https://doi.org/10.3310/hta18430 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071139425
217 rdf:type schema:CreativeWork
218 https://doi.org/10.3310/hta20840 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071139640
219 rdf:type schema:CreativeWork
220 https://www.grid.ac/institutes/grid.13097.3c schema:alternateName King's College London
221 schema:name Department of Primary Care and Public Health Sciences, King’s College, London, UK
222 rdf:type schema:Organization
223 https://www.grid.ac/institutes/grid.425213.3 schema:alternateName St Thomas' Hospital
224 schema:name Department of Primary Care and Public Health Sciences, King’s College, London, UK
225 NIHR Biomedical Research Centre at Guy’s and St Thomas’ Hospital, London, UK
226 rdf:type schema:Organization
227 https://www.grid.ac/institutes/grid.7445.2 schema:alternateName Imperial College London
228 schema:name Department of Primary Care and Public Health Sciences, King’s College, London, UK
229 Imperial Clinical Trials Unit, Imperial College London, 68 Wood Lane, W12 7RH, London, UK
230 rdf:type schema:Organization
231 https://www.grid.ac/institutes/grid.83440.3b schema:alternateName University College London
232 schema:name Department of Behavioural Science and Health, University College, London, UK
233 Department of Primary Care and Public Health Sciences, King’s College, London, UK
234 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...