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Record W1767415911 · doi:10.24908/pceea.v0i0.5817

ENGINEERING IN MEDICINE

2015· article· en· W1767415911 on OpenAlexaffvenue
Amy Hsiao, Andrew Smith

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2015
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMultidisciplinary approachWork (physics)Medical educationScience and engineeringEngineering educationEngineering ethicsComputer scienceEngineering managementMedicineEngineeringSociologyMechanical engineering

Abstract

fetched live from OpenAlex

This work presents a description and assessment of the first introductory course in biomedical engineering, the result of a two-year exchange between clinicians and researchers in the Faculties of Medicine and Engineering and Applied Science. The course is an integration of research into teaching, as well as the first step in incorporating engineering into Medicine. Through lectures, clinical shadowing, tours, critical reflection and a final project, students interested in healthcare experienced a unique, multidisciplinary learning environment in a seminar-style setting. The work will present student assignments, student feedback and student application of the key concepts in the course. An assessment of the defined learning outcomes for the course is also addressed. In presenting this work, the authors would also like to promote knowledge sharing of how engineering and medicine are integrated at other universities in the area of Biomedical Engineering, specifically as a major, a sub-discipline, or a multidisciplinary undergraduate degree.

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)0.003
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.057
Threshold uncertainty score0.190

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.006
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0570.023

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 head0.196
Teacher spread0.187 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

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