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Record W4367184394 · doi:10.24908/pocus.v8i1.16153

Recommendations for POCUS Curriculum in Canadian Undergraduate Medical Education: Consensus from the Inaugural Seguin Canadian POCUS Education Conference

2023· article· en· W4367184394 on OpenAlexafffundvenueabout
Sacha Weill, Daniel Picard, Daniel Kim, Michael Y. Woo

Bibliographic record

VenuePOCUS Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsVancouver General HospitalOttawa HospitalUniversity of British ColumbiaUniversity of Ottawa
FundersCanadian Association of Emergency Physicians
KeywordsCurriculumMedical educationPoint of care ultrasoundMedicinePsychologyPedagogyNursing

Abstract

fetched live from OpenAlex

Point of care ultrasound (POCUS) in Canadian undergraduate medical education (UGME) is limited. To address this paucity, the inaugural Seguin Canadian POCUS Education Conference hosted 14 of the 17 Canadian medical schools to develop a list of recommendations for POCUS education in Canadian UGME. Attending schools were divided into delegations consisting of a pre-clerkship student, a clerkship student and a staff physician. Recommendations were developed via a modified consensus development panel. Delegations submitted school-specific POCUS education summary documents for roundtable discussions, which yielded an initial set of recommendations. These were then summarized in a large group setting and voted upon for adoption with an a priori agreement threshold of 80%. Conference attendees developed 14 recommendations which 87% of participants agreed to adopt. Conference recommendations reflect the opinions of Canadian trainees and POCUS education experts thus serving as a framework for UGME POCUS education in Canada.

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)0.093
metaresearch head score (Gemma)0.166
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score0.612

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0930.166
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0080.007
Science and technology studies0.0070.003
Scholarly communication0.0060.003
Open science0.0090.007
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0050.002

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 head0.051
GPT teacher head0.382
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 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
GenreOther

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

Citations16
Published2023
Admission routes4
Has abstractyes

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