Bibliographic record
Abstract
Introduction:Robotic surgery has rapidly integrated into our Canadian healthcare system for the management of surgical diseases. However, the benefits in patient-important outcomes from robotic surgery compared with laparoscopic or open procedures remain unclear and controversial. The objective of this thesis is to evaluate robotic surgery in Ontario and the public’s perception of this new technology. Methods: Using linked health administrative data, we identified adults who received the most common robotic surgeries in Ontario, which include radical prostatectomy, total hysterectomy, thoracic lobectomy, and partial nephrectomy, since the beginning of documenting robotic surgery in administrative databases the province, 2008-2018. We first determined the trend in the utilization of the robotic approach compared to the non-robotic approach. Subsequently, we examined the patient and system-level characteristics that could determine the receipt of robotic compared to laparoscopic and open approaches. Then we compared 90-day perioperative adverse events across the 3 surgical approaches for all procedures. Finally, we examined the public’s perception of and preference for robotic surgery. Results: We demonstrated that the use of robotic surgery has increased. Lower comorbidity burden, earlier disease stage (among cancer cases), higher surgeon case volume, early career surgeons, and surgery at a teaching hospital, were consistently associated with receipt of robotic surgery. The robotic approach was associated with fewer 90-day adverse events compared to the open approach. However, the robotic approach was not associated with 90-day adverse events compared to the laparoscopic approach. More participants feared outcomes of robotic surgery more than laparoscopic surgery, while participants preferred laparoscopic over robotic surgery. Conclusion: The trend of robotic surgery has increased and is offered to a selected group of patients by selected surgeons. The benefit of robotic surgery is related to the minimally invasive approach rather than the platform itself. The public prefers laparoscopic surgery over robotic surgery and is more fearful of undergoing surgery using the robotic approach. The rapid implementation of robotic surgery is less likely to be due to the public’s preference and is more likely related to other factors, such as marketing or system-level factors. The study of additional real-world clinical outcomes and associated costs is needed before further expanding use among additional providers and hospitals.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.024 | 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".