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Record W4309785590 · doi:10.36834/cmej.74246

Enhanced point of care ultrasound skills after additional instruction from simulated patients

2022· article· en· W4309785590 on OpenAlexafffundvenueabout
Paul Olszynski, Bryan D Johnston, Danielle McIntyre, Krista Trinder

Bibliographic record

VenueCanadian Medical Education Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsSaskatchewan HealthUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsPoint of care ultrasoundPoint (geometry)Computer scienceUltrasoundMedicineRadiologyMathematics

Abstract

fetched live from OpenAlex

Background: Point of Care Ultrasound (POCUS) training in Canadian undergraduate medical programs is steadily increasing. To date, the simulated patients (SPs) in our program have only provided feedback on comfort and professionalism. Involving the POCUS SPs as teachers (SP-teachers) of POCUS skills provides an additional opportunity for instruction. In this pilot study, we explored the impact of SP-teachers instructing medical trainees while they learned POCUS. Outcomes of interest included the level of proficiency achieved after the session and trainee satisfaction with the learning experience. Methods: Second year medical students were randomized into a conventional or SP-teacher learning experience. Both groups received the same video tutorial, instructor guidance, and basic SP feedback (comfort and professionalism). The SP-teaching group received additional instruction (landmarks, transducer technique, and troubleshooting) from the SP-teachers when session instructors were assisting others. Students evaluated the session and were subsequently assessed through direct observation. Results: = 0.002, d =1.75). Both groups rated their sessions highly. Conclusions: Students that received SP-teaching were observed to better acquire images and achieved higher entrustment scores. In this pilot study, SP-teachers had a positive effect on acquisition of POCUS 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 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)0.000
metaresearch head score (Gemma)0.010
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 score0.529
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.4440.000

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 head0.005
GPT teacher head0.280
Teacher spread0.275 · 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

Citations5
Published2022
Admission routes4
Has abstractyes

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