MétaCan
Menu
Back to cohort

The unintentional effects on body donation programs of a competency‐based curriculum in post‐graduate medical education

2020· article· en· W3016502496 on OpenAlexaff
Geoffroy Noël, Joseph B. Dubé, Gabriel Venne

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsMcGill University
Fundersnot available
KeywordsCurriculumMedical educationDonationBody positionOrgan donationMedicinePsychologyPedagogyTransplantationSurgeryPolitical science

Abstract

fetched live from OpenAlex

A competency‐based curriculum shifts the focus from acquiring knowledge to using knowledge, placing emphasis upon the practices used by professionals and encouraging students to utilize their medical knowledge for specific tasks performed within realistic settings. As medical programs place increasing importance on competency‐based training and surgical simulations for residents, anatomy laboratories and body donation programs find themselves in a position of adapting to changing needs and demands. These skill‐centered curricula exhibit a growing demand for simulation facilities as well as specimens with more realistic properties, and institutions must assess how current or new teaching and embalming techniques respond to the demands of the residency programs they serve. Similarly, institutions’ body donation programs must adapt to a competency‐based curriculum’s greater need for cadaveric specimens, which presents many challenges to a body donation program, ranging from the logistical to the ethical.

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)0.019
metaresearch head score (Gemma)0.100
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.100
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0020.006
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0140.001

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 head0.010
GPT teacher head0.243
Teacher spread0.234 · 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 designObservational
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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicAnatomy and Medical TechnologyFrench-language works237,207