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Record W2317788471 · doi:10.5603/cj.2014.0009

Generation iUltrasound: Bedside diagnostic imaging with Hand-Held Cardiac Ultrasound

2014· article· en· W2317788471 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCardiology Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineCardiologyCardiac imagingInternal medicineUltrasoundCardiac UltrasoundUltrasound imagingRadiology

Abstract

fetched live from OpenAlex

In the early nineteenth century, an enquiring physician rolled up a notebook and placed it upon the chest of a young woman in order to hear her heart and understand its rhythmic beating more clearly. Thus began the teaching of a new approach to ‘les maladies cardiologiques’ [1]. Two-hundred years later this tube remains the cornerstone of the cardiac physical exam; an object evocative of our profession (Figs. 1, 2). As clinical educators, are we ready for a change in diagnostic technology? Whether we are or not, the availability of small, portable Hand-Held Cardiac Ultrasound (HHCU) devices portends a tidal change in diagnostic ability with important implications to the physical exam and point-of-care diagnosis (Fig. 3). We are now tasked with establishing the capabilities and limitations of these new medical devices in the hands of various users. Some consensus is emerging: studies have shown that these devices are a valuable tool for augmenting the diagnostic value of the standard physical exam and even novel learners can be taught to identify specific cardiac pathologies using HHCU [2–4]. However it is now clearly recognized that bedside examination with HHCU is not to be considered a substitute for a comprehensive or limited echocardiogram, and with the current technology these machines are only suited to performing focused cardiac ultrasound examinations, even when used by expert sonographers [5]. Within the practice of focused cardiac ultrasound, the image acquisition and interpretation skills of the user are the major factors determining the efficacy of these devices as a mechanism for improving the diagnostic power of a standard cardiac exam [6]. Further research into the development and implementation of HHCU training is warranted. The Cardiovascular Imaging Network at Queen’s (CINQ) has sought to gain experience in developing a critical framework for teaching HHCU skills in new learners. Our group is led by expert Level III trained echocardiographers with expertise in medical education. Our work has been presented at several conferences across Canada and the USA [2, 7] and we lead the Canadian National Working Group on HHCU Education. We recently advanced this work by conducting a preliminary study in 10 senior internal medical residents demonstrating that HHCU improved clinical management of patients in the Emergency Department and improved the confidence of these physicians in their assessment of patients referred for cardiology assessment [8]. Although this exciting preliminary work shows feasibility of a formal HHCU training program for residents, careful further study is required to investigate the optimal method of delivering this integrative skill. Acquisition of HHCU skill is becoming recognized as an emerging essential competency, however, currently medical residency has objectives set by the Royal College which do not incorporate these specific skills. Thus, additional training of this complex integrative skill requires recognition of the current time and resource constraints placed upon learners and educators. To address these challenges, we developed rich electronic modules in coordination with our Information Technology experts at Queen’s School of Medicine, and worked with our Simulation Laboratory to acquire and develop a state-of-the-art cardiac ultrasound simulation program. In addition to the lecture based curriculum developed by expert educators in our laboratory, we developed resources available for delivering HHCU programming in post-graduate training. We now intend to look at various combinations of these resources to determine the optimal method of delivering HHCU skill to internal medicine trainees during their busy residency. COMMENTARIES Cardiology Journal 2014, Vol. 21, No. 1, pp. 98–99 DOI: 10.5603/CJ.2014.0009 Copyright © 2014 Via Medica ISSN 1897–5593

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.302
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it