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Record W3008581591 · doi:10.1093/jcag/gwz047.088

A89 IMPLEMENTING ENDOSCOPIC SUBMUCOSAL DISSECTION IN A WESTERN CANADIAN SETTING: OUTCOMES, LEARNING CURVE AND LOGISTICAL CONSIDERATIONS

2020· article· en· W3008581591 on OpenAlexaffabout
Roberto Trasolini, Bixiao Zhao, Daljeet Chahal, Eric Lam

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2020
Typearticle
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineEndoscopic submucosal dissectionPerforationInterquartile rangeEndoscopic ultrasoundEndoscopic mucosal resectionSurgeryIncidence (geometry)EndoscopyGeneral surgery

Abstract

fetched live from OpenAlex

Abstract Background Endoscopic submucosal dissection (ESD) is an advanced resection technique for large gastrointestinal lesions. ESD was developed in Japan and is popular in countries with gastric cancer screening and a high incidence of gastric cancer. ESD has benefits over endoscopic mucosal resection (EMR) such as increased complete resection, en bloc resection and lower recurrence. However, ESD is a longer procedure and is difficult to master in countries with low incidence of early gastric neoplasia which is the ideal anatomic location for learning. There is increasing interest in using ESD techniques including hybrid ESD/EMR in western centers. Barriers include procedure time, perforation risk and challenges accumulating sufficient experience. Aims To present our experience implementing an ESD program in British Columbia including outcomes and logistical considerations of interest. Methods All ESD procedures since implementation of the program in May 2015 to July 2019 were included. Descriptive statistics and performance indicators over time are reported. All procedures were performed by a staff endoscopist after specialized training. Procedures were performed at two hospitals in British Columbia. Cases were referred from endoscopists and were assessed with dedicated endoscopy with or without endoscopic ultrasound prior to booking ESD. Results 40 procedures were performed, though only one procedure was performed in the first year (Mean age 70, 67.5% male). ASA class ranged from 1–4 (mean 2.08). 22 lesions were gastric, 13 were rectal, with the remainder throughout the colon. Mean lesion size was 25mm in maximum dimension (interquartile range 15-30mm). 18 procedures were performed under general anesthesia and the remainder using procedural sedation. Total surgical time ranged from 22 to 398 minutes. Mean surgical time was 104 minutes, or 126 minutes including anesthesia. 50% of procedures were performed using hybrid ESD/EMR technique. R0 resection rate across all cases was 68% (60% for hybrid procedures, 80% for strict ESD). En bloc resection rate was 60%. Recurrence rate was 10%. Complication rate was 7.5% all were post-procedure bleeds requiring hospitalization. No perforations occurred. 3 patients required surgery for incomplete resection or invasive cancer on pathology, 3 required repeat endoscopic resection. Surgical time per cm of lesion improved significantly from the first 10 cases to the last 10 (time per cm resected 75 min to 32 min p<0.006). Conclusions ESD is an effective therapy for GI neoplasia. ESD is feasible in a Canadian setting. Hybrid techniques tend to be faster though at the expense of R0 resection. Patient centered outcomes in this sample are favorable and comparable to large ESD series. Monitoring of ESD quality is critical for comparison with standard of care as experience with ESD in Canada grows. Funding Agencies None

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.019
GPT teacher head0.269
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2020
Admission routes2
Has abstractyes

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