MANAGEMENT OF SUPERFICIAL RECTAL TUMORS BY SUBMUCOSAL DISSECTION: A CANADIAN EXPERIENCE
Bibliographic record
Abstract
Aims Our objective is to evaluate ESD efficacy in a Canadian practice. Methods Retrospective analysis of consecutive patients that underwent ESD procedure between 07/2017 and 10/2019. Results 20 patients (mean age 67 yo (50–79), sex ratio = 12 H/8 F) were included. 13 tumors had a mixed granular morphology while 7 tumors had a non granular morphology. Paris classification were 0-Is (n = 8), 0-IIa (n = 3), 0-IIa + Is (n = 6), 0-IIa + c (n = 3), and had Kudo pattern III (n = 4), IV (n = 13) or V (n = 3). Average procedure time was 157 minutes (73–473). 15 (75%) of ESD resection were en bloc, 4 (20%) by fragmented endoscopic mucosal resection (EMR) and 1 (5%) by surgery. The pathology revealed 2 LGD adenoma, 9 HGD adenoma and 7 adenocarcinoma (4 intra-mucosal, 2 sm1 and 1 sm3) The average pathology tumor length was 60 mm (13–110; n = 15). An adverse event happened in 9 cases (45%): 4 perforations were treated endoscopically, 4 urinary retentions and 1 hemorrhoidal thrombosis were treated medically. Average length of hospital stay was 2,1 days (1–5). The resection was R0 and curative in 9/20 (45%). The resections weren’t curative because of a positive lateral marge (5), a positive deep marge (1), ESD failure (1) or P-EMR (4). 10 patients had an endoscopic follow up (average = 37 weeks post-ESD). 1 patient had an adenoma recurrence. Conclusions ESD is a difficult endoscopic technique, but it allows a good treatment of advanced rectal lesions with a low recurrence rate.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| 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".