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Record W2152103807 · doi:10.36834/cmej.36619

Medical educators' perspectives of teaching physical examinations using ultrasonography at the undergraduate level

2013· article· en· W2152103807 on OpenAlexaffvenueabout
Irene Ma, Ian Wishart, Malgorzata E. Kaminska, Kevin McLaughlin, Sarah Weeks, David Lautner, Heather Baxter, Bruce Wright

Bibliographic record

VenueCanadian Medical Education Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineUltrasonographyCurriculumPhysical examinationPericardial effusionMedical educationRadiologyMedical physicsSurgeryPsychologyPedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: Ultrasonography is increasingly used for teaching physical examination in medical schools. This study seeks the opinions of educators as to which physical examinations would be most enhanced by the addition of ultrasonography. We also asked when ultrasound-aided physical examination teaching could have deleterious effects if used outside its intended scope. METHODS: All of the educators from the University of Calgary Master Teacher Program were invited to complete a 22-item paper-based survey. Survey items were generated independently by two investigators, with input from an expert panel (n = 5). RESULTS: Of the 36 educators, 27 (75%) completed the survey. Examinations identified to be potentially most useful included: measuring the size of the abdominal aorta, identifying the presence/absence of ascites, identifying the presence/absence of pleural effusions, and measuring the size of the bladder. Examinations thought to be potentially most harmful included: identifying the presence/absence of intrauterine pregnancy, measuring the size of the abdominal aorta, and identifying the presence/absence of pericardial effusion. CONCLUSIONS: Examinations that are potentially the most useful may also be potentially the most harmful. When initiating an ultrasound curriculum for physical examinations, educators should weigh the risks and benefits of examinations chosen.

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 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)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0230.000

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 head0.031
GPT teacher head0.362
Teacher spread0.331 · 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 teacher head, not a consensus.

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

Citations15
Published2013
Admission routes3
Has abstractyes

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