Use of Colonic Stents in Emergent Malignant Left Colonic Obstruction: A Markov Chain Monte Carlo Decision Analysis
Bibliographic record
Abstract
PURPOSE: This decision analysis examines the cost-effectiveness of colonic stenting as a bridge to surgery vs. surgery alone in the management of emergent, malignant left colonic obstruction. METHODS: We used a Markov chain Monte Carlo decision analysis model to determine the effect on health-related quality of life of two strategies: emergency surgery vs. emergency colonic stenting as a bridge to definitive surgery. All relevant health states were modeled during a patient's expected lifespan. Outcome measures were mortality, the proportion of patients requiring a colostomy, quality-adjusted life expectancy, and costs. Deterministic and probabilistic sensitivity analyses were performed. RESULTS: In our model, colonic stenting was more effective (9.2 quality-adjusted life months benefit) and less costly (CAD dollars 3,763; US dollars 3,135) than emergency surgery. Its benefits were secondary to reductions in acute mortality and in the likelihood of requiring a permanent colostomy. The results were only dependent on the rate of stenting complications (perforation, technical placement failure, and migration) and the patient's risk of surgical mortality, with the benefits being greatest among patients at high risk of operative mortality. CONCLUSIONS: Colonic stenting as a bridge to surgery is more effective and less costly than surgery in the treatment of emergent, malignant left colonic obstruction. The benefits are most pronounced in high-risk patients and are diminished by increases in stent placement failure rates and perforation rates. In low-risk patients, the benefits are more modest and may not outweigh the risks.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".