Wide-field endoscopic mucosal resection versus endoscopic submucosal dissection for laterally spreading colorectal lesions: a cost-effectiveness analysis
Bibliographic record
Abstract
OBJECTIVE: To compare the cost-effectiveness of endoscopic submucosal dissection (ESD) and wide-field endoscopic mucosal resection (WF-EMR) for removing large sessile and laterally spreading colorectal lesions (LSLs) >20 mm. DESIGN: An incremental cost-effectiveness analysis using a decision tree model was performed over an 18-month time horizon. The following strategies were compared: WF-EMR, universal ESD (U-ESD) and selective ESD (S-ESD) for lesions highly suspicious for containing submucosal invasive cancer (SMIC), with WF-EMR used for the remainder. Data from a large Western cohort and the literature were used to inform the model. Effectiveness was defined as the number of surgeries avoided per 1000 cases. Incremental costs per surgery avoided are presented. Sensitivity and scenario analyses were performed. RESULTS: 1723 lesions among 1765 patients were analysed. The prevalence of SMIC and low-risk-SMIC was 8.2% and 3.1%, respectively. Endoscopic lesion assessment for SMIC had a sensitivity and specificity of 34.9% and 98.4%, respectively. S-ESD was the least expensive strategy and was also more effective than WF-EMR by preventing 19 additional surgeries per 1000 cases. 43 ESD procedures would be required in an S-ESD strategy. U-ESD would prevent another 13 surgeries compared with S-ESD, at an incremental cost per surgery avoided of US$210 112. U-ESD was only cost-effective among higher risk rectal lesions. CONCLUSION: S-ESD is the preferred treatment strategy. However, only 43 ESDs are required per 1000 LSLs. U-ESD cannot be justified beyond high-risk rectal lesions. WF-EMR remains an effective and safe treatment option for most LSLs. TRIAL REGISTRATION NUMBER: NCT02000141.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".