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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".