Ontology type: schema:ScholarlyArticle
2019-12
AUTHORSBrian P. Lucas, Antonietta D’Addio, Clay Block, Harold L. Manning, Brian Remillard, James C. Leiter
ABSTRACTCurrent methods of assessing competence in acquiring point-of-care ultrasound images are inadequate. They rely upon cumbersome rating systems that do not depend on the actual outcome measured and lack evidence of validity. We describe a new method that uses a rigorous statistical model to assess performance of individual trainees based on the actual task, image acquisition. Measurements obtained from the images acquired (the actual desired outcome) are themselves used to validate effective training and competence acquiring ultrasound images. We enrolled a convenience sample of 21 spontaneously breathing adults from a general medicine ward. In random order, two trainees (A and B) and an instructor contemporaneously acquired point-of-care ultrasound images of the inferior vena cava and the right internal jugular vein from the same patients. Blinded diameter measurements from each ultrasound were analyzed quantitatively using a multilevel model. Consistent mean differences between each trainee’s and the instructor’s images were ascribed to systematic acquisition errors, indicative of poor measurement technique and a need for further training. Wider variances were attributed to sporadic errors, indicative of inconsistent application of measurement technique across patients. In addition, the instructor recorded qualitative observations of each trainee’s performance during image acquisition. For all four diameters, the means and variances of measurements from trainee A’s images differed significantly from the instructor’s, whereas those from trainee B’s images were comparable. Techniques directly observed by the instructor supported these model-derived findings. For example, mean anteroposterior diameters of the internal jugular vein obtained from trainee A’s images were 3.8 mm (90% CI 2.3–5.4) smaller than from the instructor’s; this model-derived finding matched the instructor’s observation that trainee A compressed the vein during acquisition. Instructor summative assessments agreed with model-derived findings, providing internal validation of the descriptive and quantitative assessments of competence acquiring ultrasound images. Clinical measurements obtained from point-of-care ultrasound images acquired contemporaneously by trainees and an instructor can be used to quantitatively assess the image acquisition competence of specific trainees. This method may obviate resource-intensive qualitative rating systems that are based on ultrasound image quality and direct observation, while also helping instructors guide remediation. More... »
PAGES4
http://scigraph.springernature.com/pub.10.1186/s13089-019-0119-6
DOIhttp://dx.doi.org/10.1186/s13089-019-0119-6
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1112358772
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/2008/0104",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Statistics",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Mathematical Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Dartmouth College",
"id": "https://www.grid.ac/institutes/grid.254880.3",
"name": [
"Medicine Service, White River Junction VA Medical Center, White River Junction, VT, USA",
"Geisel School of Medicine at Dartmouth College, Hanover, NH, USA"
],
"type": "Organization"
},
"familyName": "Lucas",
"givenName": "Brian P.",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Dartmouth College",
"id": "https://www.grid.ac/institutes/grid.254880.3",
"name": [
"Geisel School of Medicine at Dartmouth College, Hanover, NH, USA"
],
"type": "Organization"
},
"familyName": "D\u2019Addio",
"givenName": "Antonietta",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Dartmouth College",
"id": "https://www.grid.ac/institutes/grid.254880.3",
"name": [
"Medicine Service, White River Junction VA Medical Center, White River Junction, VT, USA",
"Geisel School of Medicine at Dartmouth College, Hanover, NH, USA"
],
"type": "Organization"
},
"familyName": "Block",
"givenName": "Clay",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Dartmouth\u2013Hitchcock Medical Center",
"id": "https://www.grid.ac/institutes/grid.413480.a",
"name": [
"Geisel School of Medicine at Dartmouth College, Hanover, NH, USA",
"Department of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA"
],
"type": "Organization"
},
"familyName": "Manning",
"givenName": "Harold L.",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Dartmouth\u2013Hitchcock Medical Center",
"id": "https://www.grid.ac/institutes/grid.413480.a",
"name": [
"Geisel School of Medicine at Dartmouth College, Hanover, NH, USA",
"Department of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA"
],
"type": "Organization"
},
"familyName": "Remillard",
"givenName": "Brian",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Dartmouth\u2013Hitchcock Medical Center",
"id": "https://www.grid.ac/institutes/grid.413480.a",
"name": [
"Geisel School of Medicine at Dartmouth College, Hanover, NH, USA",
"Department of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA"
],
"type": "Organization"
},
"familyName": "Leiter",
"givenName": "James C.",
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1016/j.ajem.2004.01.004",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000450209"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1046/j.1365-2923.2003.01594.x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003236933"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/cc8200",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003791704",
"https://doi.org/10.1186/cc8200"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/jhm.503",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1004383536"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/jhm.503",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1004383536"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.2165/00007256-200030010-00001",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011076235",
"https://doi.org/10.2165/00007256-200030010-00001"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.2165/00007256-200030010-00001",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011076235",
"https://doi.org/10.2165/00007256-200030010-00001"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1111/acem.12164",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014465856"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/jcu.22173",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014833879"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.echo.2015.09.002",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1017057998"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.echo.2010.05.010",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018911221"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.7863/jum.2011.30.12.1649",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022826982"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1037/1082-989x.1.1.30",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022929073"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s0140-6736(00)04221-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1024968688"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1023/a:1022639521218",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028868793",
"https://doi.org/10.1023/a:1022639521218"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/jhm.438",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028954147"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/jhm.438",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028954147"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1080/10543400701376480",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1035793888"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1177/1742271x13513224",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036111184"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1177/1742271x13513224",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036111184"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/env.877",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1040071375"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/uog.5256",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041703681"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1080/10543400701329448",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043321693"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1177/1742271x14566847",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043540386"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1177/1742271x14566847",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043540386"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/s12909-015-0294-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044661401",
"https://doi.org/10.1186/s12909-015-0294-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/s12909-015-0294-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044661401",
"https://doi.org/10.1186/s12909-015-0294-5"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.archoralbio.2008.04.010",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048178888"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1177/000456329703400103",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048335023"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1177/000456329703400103",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048335023"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1177/096228029200100202",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050223472"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1177/096228029200100202",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050223472"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1097/mlr.0b013e31827a8bb0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1052914188"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1097/mlr.0b013e31827a8bb0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1052914188"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1177/016327879401700202",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053476116"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1177/016327879401700202",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053476116"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1191/0962280206sm441oa",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1064155279"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1191/0962280206sm441oa",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1064155279"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1214/06-ba117",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1064389479"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.18637/jss.v052.i11",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1068672787"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.2214/ajr.135.2.335",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1069310585"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.5603/ait.2014.0060",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1073036622"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.7326/0003-4819-155-1-201107050-00005",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1073712217"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/jcu.22491",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1085222086"
],
"type": "CreativeWork"
}
],
"datePublished": "2019-12",
"datePublishedReg": "2019-12-01",
"description": "Current methods of assessing competence in acquiring point-of-care ultrasound images are inadequate. They rely upon cumbersome rating systems that do not depend on the actual outcome measured and lack evidence of validity. We describe a new method that uses a rigorous statistical model to assess performance of individual trainees based on the actual task, image acquisition. Measurements obtained from the images acquired (the actual desired outcome) are themselves used to validate effective training and competence acquiring ultrasound images. We enrolled a convenience sample of 21 spontaneously breathing adults from a general medicine ward. In random order, two trainees (A and B) and an instructor contemporaneously acquired point-of-care ultrasound images of the inferior vena cava and the right internal jugular vein from the same patients. Blinded diameter measurements from each ultrasound were analyzed quantitatively using a multilevel model. Consistent mean differences between each trainee\u2019s and the instructor\u2019s images were ascribed to systematic acquisition errors, indicative of poor measurement technique and a need for further training. Wider variances were attributed to sporadic errors, indicative of inconsistent application of measurement technique across patients. In addition, the instructor recorded qualitative observations of each trainee\u2019s performance during image acquisition. For all four diameters, the means and variances of measurements from trainee A\u2019s images differed significantly from the instructor\u2019s, whereas those from trainee B\u2019s images were comparable. Techniques directly observed by the instructor supported these model-derived findings. For example, mean anteroposterior diameters of the internal jugular vein obtained from trainee A\u2019s images were 3.8 mm (90% CI 2.3\u20135.4) smaller than from the instructor\u2019s; this model-derived finding matched the instructor\u2019s observation that trainee A compressed the vein during acquisition. Instructor summative assessments agreed with model-derived findings, providing internal validation of the descriptive and quantitative assessments of competence acquiring ultrasound images. Clinical measurements obtained from point-of-care ultrasound images acquired contemporaneously by trainees and an instructor can be used to quantitatively assess the image acquisition competence of specific trainees. This method may obviate resource-intensive qualitative rating systems that are based on ultrasound image quality and direct observation, while also helping instructors guide remediation.",
"genre": "research_article",
"id": "sg:pub.10.1186/s13089-019-0119-6",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1042326",
"issn": [
"2036-3176",
"2036-7902"
],
"name": "Critical Ultrasound Journal",
"type": "Periodical"
},
{
"issueNumber": "1",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "11"
}
],
"name": "Clinical measurements obtained from point-of-care ultrasound images to assess acquisition skills",
"pagination": "4",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"d3653d6e65b66a19fafd1f0d9a4edc1cd2244304f019bf0e4c1ccdd70e21a66b"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1186/s13089-019-0119-6"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1112358772"
]
}
],
"sameAs": [
"https://doi.org/10.1186/s13089-019-0119-6",
"https://app.dimensions.ai/details/publication/pub.1112358772"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-11T14:00",
"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/v90/0000000371_0000000371/records_130826_00000006.jsonl",
"type": "ScholarlyArticle",
"url": "https://link.springer.com/10.1186%2Fs13089-019-0119-6"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
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/s13089-019-0119-6'
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/s13089-019-0119-6'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s13089-019-0119-6'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s13089-019-0119-6'
This table displays all metadata directly associated to this object as RDF triples.
198 TRIPLES
21 PREDICATES
60 URIs
19 LITERALS
7 BLANK NODES