More work is needed on cost‐utility analyses of robotic‐assisted surgery
Bibliographic record
Abstract
OBJECTIVE: To comprehensively analyze the cost-utility of robotic surgery in clinical practice and to investigate the reporting and methodological quality of the related evidence. METHODS: Data on cost-utility analyses (CUAs) of robotic surgery were collected in seven electronic databases from the inception to July 2021. The quality of the included studies was assessed using the CHEERs and QHES checklists. A systematic review was performed with the incremental cost-effectiveness ratio as the outcome of interest. RESULTS: Thirty-one CUAs of robotic surgery were eligible. Overall, the identified CUAs were fair to high quality, and 63% of the CUAs ranked the cost-utility of robotic surgery as "favored," 32% categorized as "reject," and the remaining 5% ranked as "unclear." Although a high heterogeneity was present in terms of the study design among the included CUAs, most studies (81.25%) consistently found that robotic surgery was more cost-utility than open surgery for prostatectomy (ICER: $6905.31/QALY to $26240.75/QALY; time horizon: 10 years or lifetime), colectomy (dominated by robotic surgery; time horizon: 1 year), knee arthroplasty (ICER: $1134.22/QALY to $1232.27/QALY; time horizon: lifetime), gastrectomy (dominated by robotic surgery; time horizon: 1 year), spine surgery (ICER: $17707.27/QALY; time horizon: 1 year), and cystectomy (ICER: $3154.46/QALY; time horizon: 3 months). However, inconsistent evidence was found for the cost-utility of robotic surgery versus laparoscopic surgery and (chemo)radiotherapy. CONCLUSIONS: Fair or high-quality evidence indicated that robotic surgery is more cost-utility than open surgery, while it remains inconclusive whether robotic surgery is more cost-utility than laparoscopic surgery and (chemo)radiotherapy. Thus, an additional evaluation is required.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.213 | 0.533 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.026 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".