CSTAR, Robotics, and Minimally Invasive Surgery: An Interview with Dr. Christopher Schlachta
Bibliographic record
Abstract
Dr. Schlachta received his undergraduate and medical degrees from McGill University. With a keen interest in various types of surgery, he then completed a surgery internship at Toronto General Hospital before choosing to pursue residency in general surgery here at Western. Subsequently, he returned to Toronto for a fellowship in advanced minimally invasive surgery and subsequently worked as a staff surgeon at the Wellesley Hospital and St. Michael’s Hospital, where he was the head of the division. Finally, Dr. Schlachta was recruited back to London to serve as the medical director of Canadian Surgical Technologies & Advanced Robotics (CSTAR) in 2005. He presently holds this position, as well as cross-appointment as a Professor in the Departments of Surgery and Oncology. He has been involved in numerous Canadian and world firsts in robotic gastrointestinal surgery. We had the opportunity to speak with Dr. Schlachta to discuss his surgical practice, current research, and the technology at CSTAR.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 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".