A131 ENDOSCOPIC SUBMUCOSAL DISSECTION: EXPERIENCE AT A LOCAL CANADIAN CENTER
Bibliographic record
Abstract
Abstract Background Endoscopic Submucosal Dissection (ESD) was established in Japan. There exist few formalized training programs compared to the growing need for its expertise internationally, with many learning through observation and in-vivo training courses. According to Oyama et. al. (1), western centers conducting ESD have learning curves which were inferior when compared to leading Japanese centers. The proposed quality metrics for a proficient operator in ESD are en bloc resection rates of 90%, complications <5%, curative resection rates of 80%, and resection speed of 9cm2/hr. Whereas, the proposed quality metrics for competency are en bloc resection rates of 80% and complication rates of <10%. (1) Here, we present ESD data from a local Canadian center performed by an expert who underwent formal training in ESD at a expert Japanese center. Aims We aim to assess if internationally proposed quality metrics of an operator proficient in ESD were met. Methods We retrospectively reviewed all patients who underwent ESD between October 2016 up till September 2024 at the Kingston Health Sciences Center. 324 consecutive ESDs were performed on the esophagus, stomach, duodenum, colon and rectum. Primary outcomes include resection speed calculated as centimeters squared per hour (cm2/hr). Secondary outcomes include the number of successful en block and R0 resections, total procedure time, and procedure-related adverse events. Demographic and procedural variables were compared with descriptive statistics (mean ± SD; median, interquartile range), two-sample t-test, and chi-square test. Results 324 (62.7% male) (67.6 ± 11.8 years) consecutive ESDs were performed, consisting of esophageal (n=109), gastric (n=55), duodenal(n=1), rectal (n=80), and colonic (n=79) ESDs. The overall mean resection speed was 10.9 ± 6.9 cm2/hr. The rate of successful en bloc resections was 96%, and R0 resections were 88.6%. Curative resection rates were 82.4%. The total procedure-related adverse events were at 4%, Notably, 1 of the perforations were delayed, requiring surgical intervention. When the ESD data was divided into early (first 160 cases) and late (later 164 cases) groups, there was a significant improvement in the resection speed (10.1 ± 7.1 cm2/hr vs 11.7 ± 6.7 cm2/hr, p = 0.05), while maintaining a consistently high rate of en bloc resection (94.4% vs 98.2%) and a low rate of adverse events (4.4% vs 3.7%). Conclusions This study demonstrates that ESDs conducted after formal training not only met proficiency quality metrics from the outset, but also exhibited improvements in resection speed and en bloc resection rates as experience increased. Characteristics of ESD 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 | 0.001 |
| 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) | 0.003 | 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".