Recurrence following successful eradication of neoplasia with endoscopic mucosal resection compared with endoscopic submucosal dissection in Barrett’s esophagus: a retrospective comparison
Bibliographic record
Abstract
Abstract Background Endoscopic mucosal resection (EMR) and endoscopic submucosal dissection (ESD) are effective treatments for Barrett’s neoplasia. However, little is known about recurrence rates following these techniques. We compared long-term neoplasia recurrence rates following EMR and ESD. Methods This study included patients with Barrett’s neoplasia (high grade dysplasia/adenocarcinoma) treated between July 2019 and December 2023 at a tertiary referral center in Canada. Outcomes were residual neoplasia at first follow-up, complete remission of neoplasia (CRN), and neoplasia recurrence following CRN. Results 157 patients were included (87 EMR, 70 ESD). Compared with EMR, the ESD group had larger lesions (median 2 vs. 3 cm, P<0.05), more adenocarcinoma (85.1% vs. 94.3%, P = 0.07), and deeper submucosal invasion (T1a: 71.6% vs. 75.8%; T1b-SM1: 25.7% vs. 6.1%; T1b≥SM2: 2.7% vs. 18.2%; P<0.05). Among 124 patients with follow-up (71 EMR, 53 ESD), 84.9% of ESD-treated patients had curative resections (i.e. R0 resection with low risk for lymph node metastasis), whereas 94.4% of EMR-treated patients had deep margin R0 resection of low risk lesions. At first follow-up, residual neoplasia (14.1% vs. 11.3%) and CRN (97.2% vs. 100%) were similar in the EMR and ESD groups, but neoplasia recurrence following CRN was significantly higher with EMR (13% vs. 1.9%, P<0.05), with cumulative probability of recurrence at 3 years of 18.3% vs. 4.2%, respectively. Conclusions Neoplasia recurrence following CRN was significantly higher following EMR compared with ESD, suggesting that ESD may be superior to EMR in preventing neoplasia recurrence in Barrett’s esophagus.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".