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Record W2337447456

Introduction of a novel teaching paradigm for head and neck anatomy.

2010· article· en· W2337447456 on OpenAlexaff
Kuan-chin Jean Chen, Jordan T. Glicksman, Peter Haase, Marjorie Johnson, Kevin Fung

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsWestern University
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: Didactic head and neck anatomy teaching has been replaced by a novel self-directed, multimodal, and multidisciplinary approach at the Schulich School of Medicine and Dentistry (SSMD). OBJECTIVES: To describe the use of a novel teaching paradigm at SSMD and to enable readers to determine how this methodology may benefit medical students at other academic institutions and disciplines. DESIGN: Prospective cohort study. METHODS: The paradigm consists of multimedia learning modules to guide independent anatomy learning. Students received a case-based assignment based on the content of the learning modules to guide them through cadaveric dissections facilitated by a multidisciplinary team of surgeons and anatomists. PRIMARY OUTCOME: Postcourse survey and mean scores comparison. The survey collected data, including demographics and previous anatomic and computer-assisted learning (CAL) experiences, and focused on measuring student perception of the proposed paradigm. Secondary outcome: Correlation of demographics. RESULTS: The paradigm was successfully implemented and warmly received, but it still requires further development. Although CAL allows increased individual engagement, students still enjoy and value lectures. In addition, students view instruction by surgeons in laboratories as the most valuable component of their anatomy teaching as it not only deepened the students' understanding of anatomic structures but also provided them with the clinical relevance. Technological innovations were welcomed by the students but have not replaced their appreciation of dissection and lecures.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.958
Threshold uncertainty score0.187

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 head0.009
GPT teacher head0.224
Teacher spread0.214 · 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 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

Citations8
Published2010
Admission routes1
Has abstractyes

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