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A Visual Guide to Foregut anatomy: Using Digital Multimedia to Enhance the Learning of Human Gross Anatomy

2018· article· en· W3176305598 on OpenAlexaff
Farshad Hosseini, Vishesh Oberoi, Majid Doroudi, Lien Vo

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGross anatomyFlexibility (engineering)Presentation (obstetrics)ForegutCurriculumHuman anatomyAnatomyMultimediaMedical educationComputer scienceMedicinePsychologyRadiologyMathematics

Abstract

fetched live from OpenAlex

Introduction Knowledge of the human gross anatomy is an essential part of a medical student's education, and is equally fundamental for the practicing clinician. Traditionally, medical schools have focused solely on hands‐on cadaver dissections to teach anatomical sciences. However, with the advancements of digital multimedia, it is about time students have other resources at their disposal to enhance their learning of human anatomy. This is particularly important as students rarely have access to the cadavers outside their designated lab times and with the addition of such resources, they will always have the flexibility to strengthen their gross anatomy knowledge, at any time, in any environment. Objectives With the goal of improving students' gross anatomy learning experience, we created a visual, interactive presentation of gross anatomy of the foregut region. The aim was to give the students some background knowledge prior to attending the lab, as well as allowing them to use labeled screenshots from the videos during the lab, and as a study resource after the labs to enhance their learning. Methods The video takes the learners through all the key anatomical structures of the foregut as identified in the first year medical undergraduate curriculum, and uses simple commentary, interactive labeling, and quiz questions to optimize the student learning experience. Results Surveys were distributed amongst 290 medical students before and after they completed the lab to determine whether the videos were helpful. Over 95% of the students felt that the videos made them more prepared for the labs and also, enhanced their learning in the lab. In addition, they reported that they would like to see similar videos for future labs. Conclusion Given the positive feedback, we conclude that this visual guide served to enhance the students' gross anatomy experience, and implementing more of such resources would help with their learning and understanding of the material. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.563
Threshold uncertainty score0.364

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 integrity0.0000.001
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 head0.011
GPT teacher head0.327
Teacher spread0.316 · 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 designBench or experimental
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
Published2018
Admission routes1
Has abstractyes

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