P758 Ileorectal anastomosis vs. ileal pouch-anal anastomosis for the surgical treatment of ulcerative colitis: A Markov decision analysis
Bibliographic record
Abstract
Abstract Background Ileorectal anastomosis (IRA) in patients with ulcerative colitis (UC) results in decreased postoperative morbidity and better functional outcome but leads to increased risk for rectal cancer compared with ileal pouch-anal anastomosis (IPAA). This study aims to compare IRA with IPAA in UC, using decision analysis. Methods A Markov simulation model was designed to simulate clinical events of IRA and IPAA over a time horizon of 40 years with time cycles of 1 year (Figure 1). The base case was a 35-year-old patient with ulcerative colitis and relatively preserved rectum. Probabilities and utilities, required to populate the model, were derived from observational studies, identified after a systematic literature search using MEDLINE. Primary outcomes were life years (LY) and quality-adjusted life years (QALY). Deterministic sensitivity analyses were performed to assess the impact of changing variables on the preferred treatment. Monte Carlo probabilistic sensitivity analysis, using 10 000 samples, was performed to account for uncertainty of variables. Prevalence of rectal cancer, IPAA and IRA failure and the stoma rate were calculated at the end of the time horizon, using markov cohort analysis. Results The model resulted in lower LY (36.22 vs. 37.02) and higher QALYs (33.42 vs. 31.57) for IRA. The results of the Monte Carlo probabilistic sensitivity analysis demonstrated that IRA was the preferred treatment option in 63% of the samples, accounting for a clinical significant margin of 0.25 QALY (Figure 2). The model was also sensitive to the utility of IRA, IPAA and end-ileostomy. A higher proportion of IRA patients will develop rectal cancer (7.6% vs. 3.2%) and 43.5% of all IRA patients will end with an ileostomy as opposed to 23.0% of all IPAA patients. The study was limited by characteristics inherent to modeling studies, including assumptions necessary to build the model, data input based on best available but often limited evidence and unavoidable extra- and interpolation of data. Conclusion IRA was the preferred treatment option when quality-adjusted life years was the outcome, with higher life years for IPAA. This model highlights that both surgical strategies are useful in ulcerative colitis patients with relatively spared rectum.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| 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) | 0.007 | 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".