Residents’ Perceptions of a Novel Virtual Livestream Cadaveric Teaching Series for Musculoskeletal Anatomy Education
Bibliographic record
Abstract
ABSTRACT: Musculoskeletal anatomy education is essential to many healthcare providers but has consistently been considered difficult for various reasons. Traditional methods have focused on in-person cadaveric teaching, which became inaccessible during the COVID-19 pandemic; therefore, new teaching methods were developed to address this gap in education. This project implemented novel virtual livestream musculoskeletal anatomy teaching methodology with cadaveric prosections and evaluated the efficacy of this modality compared with traditional in-person cadaveric teaching. A targeted musculoskeletal anatomy curriculum was developed and delivered via livestream to 12 Canadian physiatry residents. Upon completing the virtual curriculum, residents completed an anonymous survey assessing this new virtual livestream cadaveric methodology compared with previous experiences with traditional in-person anatomy teaching. The survey response rate was 92%. Most participants (73%) rated the virtual livestream sessions as better than traditional in-person teaching. Reasons included better visualization of cadaveric anatomy and easy discussion among the group. T test analysis comparing both methods demonstrated the livestream method was equivalent or better across several domains. Virtual livestream teaching is a viable method for teaching the important subject of musculoskeletal anatomy. Educators should consider how to best integrate this approach into future anatomy curricula.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".