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Record W2803033711 · doi:10.1002/aet2.10103

Clinical Cadavers as a Simulation Resource for Procedural Learning

2018· article· en· W2803033711 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

VenueAEM Education and Training · 2018
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsQueen Elizabeth II Health Sciences CentreNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsCompetence (human resources)Computer scienceMedical simulationSimulation trainingFidelitySimulationPsychology

Abstract

fetched live from OpenAlex

"See one, do one, teach one" remains an unofficial, unsanctioned framework for procedural skill learning in medicine. Appropriately, medical educators have sought alternative simulation venues for students to safely learn their craft. With the end goal of ensuring competence, educational programming will require the use of valid simulation with appropriate fidelity. While cadavers have been used for teaching anatomy for hundreds of years, more recently they are being repurposed as a "high-fidelity" procedural skill learning simulation resource. Newly deceased, previously frozen, and soft-preserved cadavers, such as those used in Baltimore and Halifax, produce clinical cadavers with high physical and functional fidelity that can serve as simulators for performing many high-acuity procedures for which there is otherwise limited clinical or simulation opportunities to practice. While access and cost may limit the use of cadavers for simulation, there are opportunities for sharing resources to provide an innovative procedural learning experience using the oldest of medical simulation assets, the human body.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.095
GPT teacher head0.439
Teacher spread0.345 · 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