Endoscopic mucosal resection for high-grade dysplasia and intramucosal carcinoma: a Canadian experience
Bibliographic record
Abstract
BACKGROUND: Endoscopic mucosal resection (EMR) is increasingly being used as a first-line treatment for Barrett esophagus (BE) with high-grade dysplasia (HGD) and intramucosal adenocarcinoma (IMC). We reviewed our experience with endoscopic treatment of BE with HGD and IMC at our institution with respect to eradication rates, complications and long-term recurrence. METHODS: We performed a single-centre retrospective review of all patients referred between October 2010 and August 2014 for EMR with dysplastic BE or IMC. We performed EMR using a cap-fitted endoscope, and the procedure was repeated every 3 months until eradication or progression of disease. RESULTS: A total of 28 patients were identified: 16 with dysplastic BE (14 HGD, 1 low-grade dysplasia, 1 intermediate dysplasia) and 12 with IMC. Complete eradication of HGD was achieved in 11 of 14 (79%) patients. Three of 12 (25%) patients initially referred with suspected IMC were found to have invasive adenocarcinoma on EMR. Eradication was successful in 8 of 9 (89%) patients with true IMC, with 1 patient progressing to salvage esophagectomy. Complications occurred in 2 of 28 (7%) patients; both had esophageal strictures managed with dilatation. Median duration of follow-up was 371 days. CONCLUSION: Our experience supports the safety of EMR as a first-line treatment for patients with BE with dysplasia and IMC in early short-term follow-up.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".