Robotic-Assisted Partial Nephrectomy for Kidney Cancer: A Health Technology Assessment.
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,069 | 0,199 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,009 | 0,017 |
| Bibliométrie | 0,036 | 0,025 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».