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Record W3160288078 · doi:10.36834/cmej.72081

The “virtual OR:” creation of a surgical video-based gynaecologic surgery teaching session to improve medical student orientation and supplement surgical learning during COVID-19

2021· article· en· W3160288078 on OpenAlexaffvenueabout
Jocelyn Stairs, Baharak Amir, Brett Vair

Bibliographic record

VenueCanadian Medical Education Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSession (web analytics)PreparednessOrientation (vector space)Coronavirus disease 2019 (COVID-19)MedicineMedical educationPresentation (obstetrics)SurgeryComputer sciencePathology

Abstract

fetched live from OpenAlex

The COVID-19 pandemic resulted in changes to clinical clerkship delivery including decreased surgical exposure. The Department of Obstetrics and Gynaecology at Dalhousie University developed a novel, resident-led learning experience using a curated presentation of operative footage. This session aimed to improve medical students' orientation to the operative environment and supplement teaching on pelvic anatomy and gynaecologic surgery in response to decreased exposure during the COVID-19 pandemic. Medical students perceived this session as valuable and felt it improved their preparedness for the operating room. This initiative has the potential to improve medical student orientation to the operative environment.

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)0.004
metaresearch head score (Gemma)0.050
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.050
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0250.000

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 head0.019
GPT teacher head0.374
Teacher spread0.356 · 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.

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

Citations5
Published2021
Admission routes3
Has abstractyes

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