Introduction of a novel teaching paradigm for head and neck anatomy.
Bibliographic record
Abstract
INTRODUCTION: Didactic head and neck anatomy teaching has been replaced by a novel self-directed, multimodal, and multidisciplinary approach at the Schulich School of Medicine and Dentistry (SSMD). OBJECTIVES: To describe the use of a novel teaching paradigm at SSMD and to enable readers to determine how this methodology may benefit medical students at other academic institutions and disciplines. DESIGN: Prospective cohort study. METHODS: The paradigm consists of multimedia learning modules to guide independent anatomy learning. Students received a case-based assignment based on the content of the learning modules to guide them through cadaveric dissections facilitated by a multidisciplinary team of surgeons and anatomists. PRIMARY OUTCOME: Postcourse survey and mean scores comparison. The survey collected data, including demographics and previous anatomic and computer-assisted learning (CAL) experiences, and focused on measuring student perception of the proposed paradigm. Secondary outcome: Correlation of demographics. RESULTS: The paradigm was successfully implemented and warmly received, but it still requires further development. Although CAL allows increased individual engagement, students still enjoy and value lectures. In addition, students view instruction by surgeons in laboratories as the most valuable component of their anatomy teaching as it not only deepened the students' understanding of anatomic structures but also provided them with the clinical relevance. Technological innovations were welcomed by the students but have not replaced their appreciation of dissection and lecures.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".