A149 SAFETY AND EFFICACY OF ENDOSCOPIC SUBMUCOSAL DISSECTION FOR ESOPHAGOGASTRIC NEOPLASMS IN A CANADIAN SETTING
Bibliographic record
Abstract
Abstract Background Endoscopic submucosal dissection (ESD) is a technique that has been developed in Japan and is increasingly being adopted by western countries for treatment of superficial gastrointestinal neoplasms. Aims In this study, we aim to present the safety and efficacy of ESD for esophageal and gastric neoplasms in a Canadian setting, given the limited data regarding the outcomes of ESD in North America Methods Data of 100 patients with superficial upper GI neoplasms (esophageal and gastric) who underwent ESD between 2016 and 2022 in Kingston Health Sciences Centre, a tertiary hospital in Kingston, Ontario, were retrospectively reviewed. Demographics and lesion characteristics, ESD technique, and outcomes in terms of efficacy and safety were analyzed. Results 100 patients were included in the study. 67% of the lesions were esophageal. The median diameter was 4.6cm and median area was 11.94cm2. Outcomes were favorable with technical success 98%, en bloc resection 96%, R0 resection 89% and curative resection 80%. Upstage in pathology from index biopsy was seen in 42% of the lesions. Adverse events were infrequently encountered (8%) and included delayed bleeding, aspiration, and pain. Conclusions ESD is a safe and effective modality for accurately diagnosing and treating early and superficial esophagogastric neoplasms when performed by trained endoscopists. Further research is required, however, to increase the adoption of this technique across Canada. Procedural Details 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".