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Record W1980860210 · doi:10.5402/2013/873825

Learning Anatomy: Can Dissection and Peer-Mediated Teaching Offer Added Benefits over Prosection Alone?

2013· article· en· W1980860210 on OpenAlexaff
Lynn Ashdown, Evan Cole Lewis, Maxwell T. Hincke, Alireza Jalali

Bibliographic record

VenueISRN Anatomy · 2013
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsDissection (medical)AnatomyPsychologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

Purpose. To evaluate the impact of an optional thoracic dissection elective upon anatomy subject acquisition and determine whether peer-mediated teaching has a beneficial effect. Methodology. First year medical students' results on thoracic anatomy laboratory examinations over a five-year period were obtained. All students were taught in the laboratory using prosected specimens as part of a standard curriculum. A subset of students from each class volunteered to participate in an optional thoracic dissection. A comparison of exam performance between the two groups was made, and the results were analyzed to see if incorporating peer teaching into the elective had an impact on the students' performance on anatomy examinations. Results. With the exception of one year's results, no significant statistical difference was found in student performance on anatomy examinations between the two groups. The addition of peer teaching did not result in superior performance. Conclusion. It is believed that prosected specimens are suitable for anatomy laboratory teaching in an undergraduate medical curriculum. Our study did not reveal that an opportunity for dissection offered any added benefit in terms of exam performance. In addition, peer teaching did not affect exam performance. This study strictly compared student exam results. It did not assess the possible impact of the dissection process to influence student attitudes towards death or the development of clinically relevant visuospatial abilities and procedural skills.

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 score0.821
Threshold uncertainty score0.883

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 head0.004
GPT teacher head0.214
Teacher spread0.210 · 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

Citations15
Published2013
Admission routes1
Has abstractyes

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