A102 EUS-GUIDED GASTROENTEROSTOMY IS COST-EFFECTIVE COMPARED TO SURGICAL GASTROJEJUNOSTOMY AND ENTERAL STENTING FOR PALLIATION OF MALIGNANT GASTRIC OUTLET OBSTRUCTION
Bibliographic record
Abstract
Abstract Background Endoscopic ultrasound-guided gastroenterostomy (EUS-GE) is an emerging modality for the treatment of malignant gastric outlet obstruction (GOO), which combines the benefits of the traditional treatments by providing a robust bypass using a minimally invasive, endoscopic approach. The health economic aspects of this new treatment compared to the traditional treatment approaches are not known. Aims To conduct a cost-consequence analysis to compare the costs and benefits of using EUS-GE, surgical gastrojejunostomy (SGJ) and enteral stenting (ES) for the management of malignant GOO. Methods A decision analytical model, comprising a decision tree and a time-dependent state-transition model with three health states (no recurrent GOO, recurrent GOO, and death), was designed. The costs and benefits of the first month are included in the decision tree, after which, simulated patients enter the time-dependent state-transition model and the costs and consequences of each intervention are estimated for each monthly cycle until the end of a twelve-month time horizon. To address uncertainty around parameters, the expected values of costs and other outcomes were obtained through probabilistic analysis. The model parameters were assigned probability distributions and 10,000 Monte Carlo simulations were conducted using randomly sampled values drawn from these distributions. The outcomes were the estimated total cost and the probability of recurrent GOO in the follow-up time. We compared the incremental cost of avoiding recurrent GOO between these three strategies. A willingness-to-pay threshold of $50,000 to avoid recurrent GOO was established a priori. Results The total estimated costs for EUS-GE, SGJ, and ES, were $82,575 (SD, $16,240), $149,731 (SD, $27,254), and $77,324 (SD, $14,529), respectively. SGJ (6.84% [SD, 1.34]) had a lower GOO recurrence rate compared to EUS-GE (8.70% [SD, 1.64] and ES (23.37% [SD, 3.82]; however, when comparing SGJ with EUS-GE and ES, the incremental cost to avoid GOO recurrence was $ 3,611,345 and $37,854, respectively. The cost of avoiding GOO recurrence using EUS-GE compared to ES was $35,774. Conclusions EUS-GE has emerged as a cost-effective option in treating patients with malignant GOO. It is associated with a much lower cost per avoided recurrent GOO when compared to SGJ, and it is more effective in preventing recurrent GOO than ES at a cost that is well below the pre-set willingness-to-pay threshold of $50,000. Cumulative rate of GOO recurrence for EUS-GE, SGJ and ES Funding Agencies None
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".