Robotic-Assisted Partial Nephrectomy for Kidney Cancer: 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 partial nephrectomy for the treatment of kidney cancer (RAPN). Nephrectomy may be radical (the surgical removal of an entire kidney, nearby adrenal gland and lymph nodes, and other surrounding tissue) or partial (the surgical removal of part of a kidney or a kidney tumour). Partial nephrectomy is the gold standard surgical treatment for early kidney cancer. Our assessment included an evaluation of the effectiveness, safety, and cost-effectiveness of RAPN, as well as the 5-year budget impact for the Ontario Ministry of Health of publicly funding RAPN. It also looked at the experiences, preferences, and values of people with kidney cancer, as well as those of health care professionals who provide surgical treatment for kidney cancer. Methods: We performed a systematic literature search of the clinical evidence to retrieve systematic reviews and selected and reported results from five reviews that were recent and relevant to our research questions. We used the Risk of Bias in Systematic Reviews (ROBIS) tool to assess the risk of bias of each included systematic review. We assessed the quality of the body of evidence reported in the selected reviews 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 robotics disposables for RAPN for people with kidney cancer in Ontario. To contextualize the potential value of RAPN for people with kidney cancer, we spoke with people with lived experience of kidney cancer who had undergone either open or robotic-assisted nephrectomy, and we spoke with urologic surgeons who perform nephrectomy. Results: We included five systematic reviews in the clinical evidence review. Low-quality evidence from observational studies suggests that compared with open or laparoscopic partial nephrectomy, RAPN may decrease estimated blood loss, shorten length of hospital stay, and reduce complications (All GRADEs: Low). We identified five studies that met the inclusion criteria of our economic literature review. Most included economic studies found robotic-assisted surgical procedures to be more costly than open and laparoscopic procedures; however, the results from these studies were not applicable to the Ontario context. Assuming a moderate increase in the volume of RAPN procedures, our reference case analysis showed that the 5-year budget impact of publicly funding RAPN for people with kidney cancer would be $1.58 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 kidney cancer, as well as urologic surgeons, spoke favourably of RAPN and its perceived benefits over open and laparoscopic procedures. Conclusions: RAPN may improve clinical outcomes and reduce complications. The cost-effectiveness of RAPN for people with kidney cancer is unknown. We estimate that the 5-year budget impact of publicly funding RAPN for people with kidney cancer would be $1.58 million. People we spoke with who had lived experience of kidney cancer and had undergone RAPN reported favourably on their experiences, particularly in terms of the quick recovery, short hospital stay, and minimal pain. Conversely, those who had undergone an open procedure spoke of difficulties including pain, complications, and increased length of hospital stay. Surgeons emphasized the importance of RAPN being made available to people with kidney cancer because of the increased risks and complications associated with open partial nephrectomy.
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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 | 0.069 | 0.199 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.017 |
| Bibliometrics | 0.036 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".