DEVELOPMENT OF A MULTIDISCIPLINARY CLINICAL CADAVER PROGRAM
Bibliographic record
Abstract
The traditional fixation techniques used in many university bequeathal programs has enabled students to learn the intricacies of the human form for decades. This process has served as a hands‐on means of learning human anatomy primarily used by undergraduate learners. Postgraduate and practicing clinicians however rarely accessed this resource until the development of the Clinical Cadaver Program at Dalhousie University. Their training and skill acquisition primarily occurs at the bedside of live patients and while important, may pose a patient safety risk. To address this issue, a proportion of the donated human cadaveric specimens within the established Human Donation Program are now being prepared using a modified light‐embalming technique developed by Dalhousie University. The Department of Medical Neuroscience is now able to prepare specimens that maintain their natural tissue integrity without the normal hardening and rigidity of tissues that occur with traditional cadaveric fixation techniques. The result is a safe, very realistic high fidelity form of simulation that allows clinical practice and research in performing medical procedures using the human body. The program continues to grow as demand for access to specimens increases. It is now a recognized and critical component of an integrated provincial simulation program. Competency based learning is now a mandated educational movement in postgraduate medicine. This move requires learning opportunity exposure that in many instances are simply not available with enough frequency to ensure skill competence. The clinical cadaver program provides for safe practice in a controlled environment that will allow clinicians to attain and maintain procedural competence.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.061 | 0.013 |
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".