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Integration of ultrasound in the education programme in anatomy

2005· article· en· W2019901454 on OpenAlexaff
Eli Tumba Tshibwabwa, Hallie Groves

Bibliographic record

VenueMedical Education · 2005
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsMcMaster University Medical Centre
Fundersnot available
KeywordsHuman anatomyContext (archaeology)MedicineUltrasoundSurface anatomySession (web analytics)Medical physicsRadiologyAnatomyMedical educationComputer science

Abstract

fetched live from OpenAlex

Context and setting Medical students invest a significant amount of time studying human structure using a variety of resources. They often find it challenging to visualise the functioning living human and to apply their learning clinically. Because ultrasound imaging provides a means of visualising structure and movement in a living person, it was proposed that it could be used to enhance and facilitate the learning of functional clinical anatomy. An ultrasound learning resource was located within the anatomy area to enable students to integrate their learning through ultrasound with the study of specimens and other resources. Why the idea was necessary The purpose of the study was to determine the effect on student learning of integrated ultrasound anatomy teaching sessions. These sessions included an introduction to the technology of ultrasound imaging and the hands-on experience of performing an examination in order to provide students with the opportunity to develop basic imaging skills, and to enhance their knowledge of surface and visceral anatomy. What was done Groups of 6 first-year medical students had an integrated clinical skills, anatomy and radiology session, followed by a series of 3 90-minute ultrasound anatomy sessions provided by a radiologist during the cardiovascular and renal subunits. The sessions included an introduction to the use of the ultrasound imaging, the setting of learning objectives, guidance on the location of landmarks and the visualisation of organs and tissues, a hands-on experience conducting an ultrasound examination on a living human, a discussion of its clinical relevance and an evaluation of the session. The objectives of the examinations were to use various approaches and scans to visualise and identify specific cardiac structures, major abdominal vessels, carotid arteries and the kidneys, as well as noting the relationships of adjacent structures. Videos of related pathologies were also discussed. The students were evaluated on their ability to perform an ultrasound examination at the end of the first and the last sessions, and received formative evaluation. For this study, a 5-point scale was used: from 1 = poor (inadequate identification of ‘acoustic window’ required for visualisation of prioritised structures) to 5 = excellent (ability to detect normal and pathological surface anatomy and functional morphology on reference standards). Approximately 490 medical students from 4 consecutive classes participated. The average score at the end of the first session was 3.25 ± 0.12, compared to the last session (4.15 ± 0.16; P < 0.005). The scores from this study were not provided to the students or incorporated into their formal evaluation. The student evaluations of the sessions were consistently excellent (score of 5/5), and included highly positive comments. Evaluation of results and impact The results of this study show that the small-group problem-based ultrasound anatomy sessions for students in the Medical Program at McMaster University, which focuses on small-group problem-based active learning, are a highly effective method for facilitating student learning and significantly enhance knowledge of living clinical anatomy. In addition, they provide students with a basic understanding of ultrasound imaging and its use in clinical practice. Finally, the students value these innovative sessions highly and find that this use of ultrasound is an exciting and engaging approach to stimulate the learning of ‘living’ clinical anatomy and enhance their clinical reasoning skills.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.002

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 head0.407
Teacher spread0.382 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations119
Published2005
Admission routes1
Has abstractyes

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