Robotic and laparoscopic gynaecological surgery: a prospective multicentre observational cohort study and economic evaluation in England
Bibliographic record
Abstract
OBJECTIVE: To compare the health-related quality of life and cost-effectiveness of robot-assisted laparoscopic surgery (RALS) versus conventional 'straight stick' laparoscopic surgery (CLS) in women undergoing hysterectomy as part of their treatment for either suspected or proven gynaecological malignancy. DESIGN: Multicentre prospective observational cohort study. SETTING: Patients aged 16+ undergoing hysterectomy as part of their treatment for gynaecological malignancy at 12 National Health Service (NHS) cancer units and centres in England between August 2017 and February 2020. PARTICIPANTS: 275 patients recruited with 159 RALS, 73 CLS eligible for analysis. OUTCOME MEASURES: Primary outcome was the European Organisation for Research and Treatment of Cancer Quality of Life measure (EORTC). Secondary outcomes included EuroQol-5 Dimension (EQ-5D-5L) utility, 6-minute walk test (6MWT), NHS costs using pounds sterling (£) 2018-2019 prices and cost-effectiveness. The cost-effectiveness evaluation compared EQ-5D-5L quality adjusted life years and costs between RALS and CLS. RESULTS: No difference identified between RALS and CLS for EORTC, EQ-5D-5L utility and 6MWT. RALS had unadjusted mean cost difference of £556 (95% CI -£314 to £1315) versus CLS and mean quality adjusted life year (QALY) difference of 0.0024 (95% CI -0.00051 to 0.0057), non-parametric incremental cost-effectiveness ratio of £231 667per QALY. For the adjusted cost-effectiveness analysis, RALS dominated CLS with a mean cost difference of -£188 (95% CI -£1321 to £827) and QALY difference of 0.0024 (95% CI -0.0008 to 0.0057). CONCLUSIONS: Findings suggest that RALS versus CLS in women undergoing hysterectomy (after adjusting for differences in morbidity) is cost-effective with lower costs and QALYs. Results are highly sensitive to the usage of robotic hardware with higher usage increasing the probability of cost-effectiveness. Non-inferiority randomised controlled trial would be of benefit to decision-makers to provide further evidence on the cost-effectiveness of RALS versus CLS but may not be practical due to surgical preferences of surgeons and the extensive roll out of RALS.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".