Canadian Association of University Surgeons’ Annual Symposium. Surgical simulation: The solution to safe training or a promise unfulfilled?
Bibliographic record
Abstract
At its 2009 annual symposium, chaired by Dr. William (Bill) Pollett, the Canadian Association of University Surgeons brought together speakers with expertise in surgery and medical education to discuss the role of surgical simulation for improving surgical training and safety. Dr. Daniel Jones, of Harvard University and the 2009 Charles Tator Lecturer, highlighted how simulation has been used to teach advanced laparoscopic surgery. He also outlined how the American College of Surgeons is moving toward competency assessments as a requirement before surgeons are permitted to perform laparoscopic surgery on patients. Dr. Teodor Grantcharov, from the University of Toronto, highlighted the role of virtual reality simulators in laparoscopic surgery as well as box trainers. Dr. Peter Brindley from the University of Alberta, although a strong proponent of simulation, cautioned against an overzealous adoption without addressing its current limitations. He also emphasized simulation's value in team training and crisis resource management training. Dr. Chris de Gara, also from the University of Alberta, questioned to what extent simulators should be used to determine competency. He raised concerns that if technical skills are learned in isolation, they may become "decontextualized," and therefore simulation might become counterproductive. He outlined how oversimplification can have an "enchanting" effect, including a false sense of security. As a result, simulation must be used appropriately and along the entire education continuum. Furthermore, far more needs to be done to realize its role in surgical safety.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".