Cost Effectiveness of Mesh Prophylaxis to Prevent Parastomal Hernia in Patients Undergoing Permanent Colostomy for Rectal Cancer
Bibliographic record
Abstract
BACKGROUND: Parastomal hernia (PSH) is common after stoma formation. Studies have reported that mesh prophylaxis reduces PSH, but there are no cost-effectiveness data. Our objective was to determine the cost effectiveness of mesh prophylaxis vs no prophylaxis to prevent PSH in patients undergoing abdominoperineal resection with permanent colostomy for rectal cancer. STUDY DESIGN: Using a cohort Markov model, we modeled the costs and effectiveness of mesh prophylaxis vs no prophylaxis at the index operation in a cohort of 60-year-old patients undergoing abdominoperineal resection for rectal cancer during a time horizon of 5 years. Costs were expressed in 2012 Canadian dollars (CAD$) and effectiveness in quality-adjusted life years. Deterministic and probabilistic sensitivity analyses were performed. RESULTS: In patients with stage I to III rectal cancer, prophylactic mesh was dominant (less costly and more effective) compared with no mesh. In patients with stage IV disease, mesh prophylaxis was associated with higher cost (CAD$495 more) and minimally increased effectiveness (0.05 additional quality-adjusted life years), resulting in an incremental cost-effectiveness ratio of CAD$10,818 per quality-adjusted life year. On sensitivity analyses, the decision was sensitive to the probability of mesh infection and the cost of the mesh, and method of diagnosing PSH. CONCLUSIONS: In patients undergoing abdominoperineal resection with permanent colostomy for rectal cancer, mesh prophylaxis might be the less costly and more effective strategy compared with no mesh to prevent PSH in patients with stage I to III disease, and might be cost effective in patients with stage IV disease.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".