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Record W4323351220 · doi:10.1093/jcag/gwac036.228

A228 SAFETY AND EFFICACY OF ENDOSCOPIC RESECTION OF NON-AMPULLARY DUODENAL POLYPS AND RISK OF POLYP RECURRENCE

2023· article· en· W4323351220 on OpenAlexaffabout
Youstina Hanna, Frances Dang, S Li, Matthew Bong-Sik Kim, J. Mosko, G May, Christopher Teshima

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsSt. Michael's HospitalUniversity of CalgaryUniversity of Toronto
Fundersnot available
KeywordsMedicineContext (archaeology)Endoscopic mucosal resectionSurgeryComplicationGastroenterologyEndoscopyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Non-ampullary duodenal adenomas, which can present sporadically or in the context of a polyposis syndrome, carry a risk of progression to carcinoma in a similar sequence to colorectal adenomas. Complete endoscopic resection is recommended as first line as a less invasive alternative to surgical rsection. Identifying recurrence rates of non-ampullary duodenal polyps after endoscopic resection, and patient and polyp characteristics associated with recurrence is important in determining the best method of resection and guiding endoscopic surveillance. Purpose To determine the technical success rate of endoscopic resection of non-ampullary duodenal polyps, complication rates, rate of residual and recurrent polyps, and identify factors associated with polyp recurrence. Method All adult patients (≥18 years) that underwent endoscopic resection of non-ampullary duodenal polyps at St. Michael’s Hospital, a Canadian tertiary referral center, from January 2010 to June 2021 were retrospectively identified. Descriptive statistics were calculated for variables of interest and Chi-square, t-test or U-Mann Whitney tests were used to compare variables as appropriate. Bi-variate regression analysis was utilized to determine co-variables associated with recurrence. Result(s) A total of 300 patients underwent endoscopic resection of duodenal polyps. Table 1 describes patient demographics, polyp and procedural characteristics and characteristics associated with recurrence. Nearly all cases were technically successful (96%, n=286/299). Clinically significant intraprocedural bleeding occurred in 22% (n=65/300) of patients, and deep mural injury occurred in 3% (n=7/284) of patients. Delayed post-procedural bleeding occurred in 9% of patients (n=26/279). The median time to first surveillance EGD was 4 months. Polyp recurrence occurred in 28% (n=50/180) of patients. Of the patients with polyp recurrence, 82% (n=42/50) were successfully managed endoscopically. On univariate analysis, polyp size (OR 1.03, 95% CI 1.01-1.06), piecemeal resection (OR 1.63, 95% CI 0.17-0.82), intraprocedural bleeding (OR 2.28, 95% CI 1.09-4.74), and high-grade dysplasia (HGD) or intramucosal adenocarcinoma (IMCa) on final histology (OR 3.46, 95% CI 1.64-7.33) were significantly associated with polyp recurrence. On multivariate analysis, only HGD/IMCa on final histology was significant (OR 3.41, 95% CI 1.38-8.47). Image Conclusion(s) Endoscopic resection of duodenal polyps can be safely performed with high technical success, however recurrence is a significant concern. Advanced histology was a significant predictor of polyp recurrence and highlights the importance of accurate pre-resection endoscopic characterization to correctly identify lesions at increased risk that may benefit from alternative resection methods such as ESD or hot rather than cold EMR, and which may require closer follow-up. Future work to develop predictive models of recurrence are needed to better stratify patient risk. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared

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)0.008
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 score0.001
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.247
Teacher spread0.238 · 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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Citations0
Published2023
Admission routes2
Has abstractyes

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Same venueJournal of the Canadian Association of GastroenterologySame topicGastric Cancer Management and OutcomesFrench-language works237,207