A systematic review of full economic evaluations of robotic-assisted surgery in thoracic and abdominopelvic procedures
Bibliographic record
Abstract
This study aims to conduct a systematic review of full economic analyses of robotic-assisted surgery (RAS) in adults' thoracic and abdominopelvic indications. Authors used Medline, EMBASE, and PubMed to conduct a systematic review following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines. Fully published economic articles in English were included. Methodology and reporting quality were assessed using standardized tools. Majority of studies (28/33) were on oncology procedures. Radical prostatectomy was the most reported procedure (16/33). Twenty-eight studies used quality-adjusted life years, and five used complication rates as outcomes. Nine used primary and 24 studies used secondary data. All studies used modeling. In 81% of studies (27/33), RAS was cost-effective or potentially cost-effective compared to comparator procedures, including radical prostatectomy, nephrectomy, and cystectomy. Societal perspective, longer-term time-horizon, and larger volumes favored RAS. Cost-drivers were length of stay and equipment cost. From societal and payer perspectives, robotic-assisted surgery is a cost-effective strategy for thoracic and abdominopelvic procedures.Clinical trial registration This study is a systematic review with no intervention, not a clinical trial.
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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 | 0.019 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".