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Expanding Program Specific Anatomy Education During Residency: New Opportunities and Challenges

2015· article· en· W1486016923 on OpenAlexaff
Anne Agur

Bibliographic record

VenueThe FASEB Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCurriculumPresentation (obstetrics)Medical educationInclusion (mineral)Residency trainingGross anatomyMedicineComponent (thermodynamics)AnatomyPsychologyRadiologyPedagogyContinuing education

Abstract

fetched live from OpenAlex

The anatomy component of medical school curricula is a dynamic entity that continues to evolve. The challenge for anatomists is to be able to deliver succinct curricula in an ever‐changing environment where hours have already been dramatically reduced and the remaining time redistributed in various ways to provide a longitudinal learning experience for the medical students. This has resulted in increased demands for specialized program specific anatomy education during residency. The focus of this presentation will be on strategies to integrate anatomy education into residency training in a changing curricular landscape and to delve into best practice scenarios that can be used to design and implement the programs. Course design and content, ongoing assessment of knowledge acquisition, anatomy and clinical faculty collaboration, role of the residents and inclusion of case based content will be discussed.

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)0.000
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 score0.983
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.085
GPT teacher head0.287
Teacher spread0.202 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations0
Published2015
Admission routes1
Has abstractyes

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