Single operator experience of endoscopic submucosal dissection for Barrett's neoplasia in a North American academic center
Bibliographic record
Abstract
Abstract Objectives Endoscopic submucosal dissection (ESD) is carving out an increasing role in the treatment of Barrett's associated neoplasia. ESD provides the advantage of en‐bloc resections and greater R0 resection rates. We aim to present outcomes from one of the largest single‐center cohorts of esophageal ESD in North America. Methods All patients undergoing esophageal ESD for Barrett's neoplasia between Oct 2016 and June 2020 at a Canadian tertiary care center were included. Demographic, procedural data, and lesion characteristics are presented. Subgroup analysis was performed on patients who underwent extensive resection (≥75% of esophageal circumference) and the patients who developed strictures. Results Thirty‐four patients were included in the series. The median lesion diameter was 5.7 cm and the median procedure time was 129 min. The en‐bloc resection rate was 97%, and the R0 resection rate was 91%. Curative resection was achieved in 82% of patients. Upstaging in histology occurred in 59% of cases. Two adverse events occurred, and there were no perforations. Procedural outcomes were similar in patients with extensive resections, but those with ≥75% circumferential resection developed more strictures (65% vs. 6.3%, p < 0.01). Stricture formation was associated with extensive resection (odds ratio [OR]: 27.5, p < 0.01) and longer lesion diameter (OR: 1.7, p = 0.02). Conclusion Our experience with ESD for Barrett's related neoplasia shows excellent en‐bloc and R0 resection rate, and provides more accurate histological specimens. Curative resection is possible in the majority of cases, including those with extensive resections. Further investigation into stricture prophylaxis will be useful as near circumferential resections are attempted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".