Robotic-Assisted Hysterectomy for Endometrial Cancer in People With Obesity: A Health Technology Assessment.
Bibliographic record
Abstract
Background: Robotic-assisted surgery has been used in Ontario hospitals for over a decade, but there is no public funding for the robotic systems or the disposables required to perform robotic-assisted surgeries ("robotics disposables"). We conducted a health technology assessment of robotic-assisted hysterectomy (RH) for the treatment of endometrial cancer in people with obesity. Our assessment included an evaluation of the effectiveness, safety, and cost-effectiveness of RH, as well as the 5-year budget impact for the Ontario Ministry of Health of publicly funding RH. It also looked at the experiences, preferences, and values of people with endometrial cancer and obesity, as well as those of health care professionals who provide surgical treatment for endometrial cancer. Methods: We performed a systematic literature search of the clinical evidence to identify systematic reviews and randomized controlled trials relevant to our research question. We reported the risk of bias from the included systematic review. We assessed the quality of the body of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. We performed a systematic economic literature search. We also analyzed the 5-year budget impact of publicly funding RH (including total, partial, and radical procedures) for people with endometrial cancer and obesity in Ontario. To contextualize the potential value of RH for people with endometrial cancer and obesity, we spoke with people with lived experience of endometrial cancer and obesity who had undergone minimally invasive surgery (either laparoscopic hysterectomy [LH] or RH), and we spoke with gynecological cancer surgeons who perform hysterectomy. Results: showed that a higher proportion of patients who underwent LH required conversion to OH compared with patients who underwent RH (7.0% vs. 3.8%, respectively) (GRADE: Very low). Rates of perioperative complications were similarly low for both LH and RH (≤ 3.5%) (GRADE: Very low). We identified two studies that met the inclusion criteria of our economic literature review. The included economic studies found RH to be more costly than OH or LH for endometrial cancer; however, because these studies were conducted in other countries, the results were not applicable to the Ontario context. Assuming a moderate increase in the volume of robotic-assisted surgeries, our reference case analysis showed that the 5-year budget impact of publicly funding RH for people with endometrial cancer and obesity would be $1.14 million. The budget impact analysis results were sensitive to surgical volume and the cost of robotics disposables. The people we spoke with who had lived experience of endometrial cancer and obesity, as well as gynecological cancer surgeons, spoke favourably of RH and its perceived benefits over OH and LH for people with endometrial cancer and obesity. Conclusions: ). Rates of perioperative complications were similarly low for both LH and RH. The cost-effectiveness of RH for people with endometrial cancer and obesity is unknown. We estimate that the 5-year budget impact of publicly funding RH for people with endometrial cancer and obesity would be $1.14 million. People we spoke with who had lived experience of endometrial cancer and obesity reported favourably on their experiences with minimally invasive hysterectomy (either LH or RH) and emphasized the importance of the availability of safe surgical options for people with obesity. Gynecological surgeons perceived RH as a superior alternative to OH and LH for people with endometrial cancer and obesity.
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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 | 0.042 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".