Cost-analysis and quality of life after laparoscopic and robotic ventral mesh rectopexy for posterior compartment prolapse: a randomized trial
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to assess, whether robotic-assistance in ventral mesh rectopexy adds benefit to laparoscopy in terms of health-related quality of life (HRQoL), cost-effectiveness and anatomical and functional outcome. METHODS: A prospective randomized study was conducted on patients who underwent robot-assisted ventral mesh rectopexy (RVMR) or laparoscopic ventral mesh rectopexy (LVMR) for internal or external rectal prolapse at Oulu University Hospital, Finland, recruited in February-May 2012. The primary outcomes were health care costs from the hospital perspective and HRQoL measured by the 15D-instrument. Secondary outcomes included anatomical outcome assessed by pelvic organ prolapse quantification method and functional outcome by symptom questionnaires at 24 months follow-up. RESULTS: There were 30 females (mean age 62.5 years, SD 11.2), 16 in the RVMR group and 14 in the LVMR group. The surgery-related costs of the RVMR were 1.5 times higher than the cost of the LVMR. At 3 months the changes in HRQoL were 'much better' (RVMR) and 'slightly better' (LVMR) but declined in both groups at 2 years (RVMR vs. LVMR, p > 0.05). The cost-effectiveness was poor at 2 years for both techniques, but if the outcomes were assumed to last for 5 years, it improved significantly. The incremental cost-effectiveness ratio for the RVMR compared to LVMR was €39,982/quality-adjusted life years (QALYs) at 2 years and improved to €16,707/QALYs at 5 years. Posterior wall anatomy was restored similarly in both groups. The subjective satisfaction rate was 87% in the RVMR group and 69% in the LVMR group (p = 0.83). CONCLUSIONS: Although more expensive than LVMR in the short term, RVMR is cost-effective in long-term. The minimally invasive VMR improves pelvic floor function, sexual function and restores posterior compartment anatomy. The effect on HRQoL is minor, with no differences between techniques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".