S1246 Endoscopic Submucosal Dissection of Gastrointestinal Superficial Neoplastic Lesions: A Canadian Single Center Experience
Bibliographic record
Abstract
Introduction: Endoscopic submucosal dissection (ESD) is an established resection technique of early gastrointestinal neoplastic lesions. However, the uptake of ESD was delayed in Canada relative to global trends due to a number of challenges within our healthcare system, which include lack of anesthesia support, delayed introduction of ESD devices, limited technical expertise and training opportunities and lack of reimbursement. Here we describe a single tertiary referral center’s experience in adopting ESD to clinical practice in Canada. Methods: All adult patients ( >18 years) who underwent ESD of a gastrointestinal lesion at St. Michael’s Hospital from October 2017 to December 2021 were retrospectively identified. Primary outcomes were rates of en-bloc, R0 and curative resection. Secondary outcomes included complication rates and recurrence rates post resection. Results: 203 patients (median age 69 years; 68% were men) underwent ESD (median lesion size 3.0 cm, interquartile range [IQR] 1.8 - 4.0) for lesions in the esophagus (n=48), stomach (n=110), duodenum (n=2), colon (n=10) and rectum (n=32). Technical success was 92% for en-bloc resection, 90% for R0 and 77% for curative resection in all patients. Median procedure time was 120 minutes (IQR 92). 95% of cases were admitted to hospital with median length of stay of 4 days (range 1-20). Major post-procedure bleeding occurred in 4% (8/203) and deep mural injury with perforation occurred in 6% (12/203). Pathology revealed 60% (124/203) of lesions were adenocarcinoma with invasion depth characterized as 64% intramucosal (80/124), 13% (17/124) SM1, 19% (24/124) SM2-SM3 and 2% (3/124) muscularis propria. Overall recurrence rate was 3% (6/203) at a mean follow-up time of 249 days(IQR 89 - 334). Conclusion: This study reports the largest single-center Canadian cohort of patients who have undergone ESD with high rates of technical success and R0 resection along with low complication rates. However, limitations specific to the Canadian health care system that have hindered the adoption of ESD have led us to take on many cases with more advanced histology, with more than one-third of our cancer patients having at least submucosal invasion. Our resource limitations mean that earlier stage lesions are still predominantly treated by EMR. We hope that our data helps to sustain the argument that more support is required to facilitate ESD in Canada, such that higher rates of curative resection will ultimately be achieved by allowing more widespread case selection.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".