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Record W4395052744 · doi:10.1055/s-0044-1782980

Delayed bleeding post-endoscopic ampullectomy for ampullary adenomas: Incidence, risk factors and management

2024· article· en· W4395052744 on OpenAlexaff
Katarzyna M. Pawlak, Samir Gupta, Kareem Khalaf, Daniel Tham, W. James Chon, J. Mosko, Christopher Teshima, Gary R. May, Nicolas Calo

Bibliographic record

VenueEndoscopy · 2024
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineMajor duodenal papillaIncidence (geometry)LesionGastroenterologyEndoscopyArgon plasma coagulationInternal medicineSurgery

Abstract

fetched live from OpenAlex

Aims The duodenal tumors of major papilla account 10% of all peri-ampullary lesions [ 1 ]. Endoscopic ampullectomy became the treatment modality for selected cases. Despite the significantly lower rate of adverse events, bleeding occurs in up to 25% of patients [ 2 ]. The rate of bleeding may be even higher, depending on periampullary lesion size and type. Factors related to delayed bleeding are poorly understood. Our study aimed to determine predicting factors for delayed post-ampullectomy bleeding. Methods We conducted a single-center retrospective study including procedures performed between January 2011 and September 2023. All patients who underwent an endoscopic papillectomy were analyzed. The primary endpoint was the incidence of delayed bleeding, which was defined as a post-procedural bleeding that necessitated either a blood transfusion, ICU admission or re-intervention. Secondary outcomes included risk factors for delayed bleeding, time to delayed bleed, management, and other adverse events. Results 113 patients underwent endoscopic papillectomy [mean age 66.2±12.2 years; male gender 51 (45.1%)]. Mean lesion size was 27.0±14.3 mm and mean procedure duration was 62.8±35.6 minutes. There were 25 cases of delayed bleeding (22.1%). Of these, 20 (80%) required repeat endoscopic intervention, 6 (24%) required blood transfusions and 3 (12%) were managed conservatively. Delayed bleeding occurred at a median of 24 hours (IQR: 6-24; Figure 1). Only 4/25 (16%) of the delayed bleeds occurred after 24 hours. The average length of hospital was longer in those experiencing a delayed bleed (8.6±4.8 vs 4.8±2.4 days, P<0.001). Delayed bleeding was greater in those with hypertension (OR 2.6, 95% CI 1.0-6.6, P=0.045), an INR≥1.2 without blood thinners (OR 11.1, 95% CI 2.6-47.2, P=0.001) or histology revealing HGD/cancer as compared with LGD (OR 3.0, 95% CI 1.08-8.11, P=0.035). A multivariate logistic regression analysis revealed that only an INR≥1.2 predicted delayed bleeding, with an OR of 13.0 (95% CI 2.5-68.0, P=0.002), after adjusting for the presence of hypertension and histopathology. There were no other predictors, including age, gender, lesion size, background anti-platelet/coagulation use, or en bloc resection. By univariate Cox proportional hazards regression, time to delayed bleeding was 5.6 times faster in those with an INR≥1.2 (HR 5.6, 95% CI: 2.0-15.5, P=0.001; Figure 2). No other factors were related to time to delayed bleeding. Other adverse events included perforation (n=7, 6.3%) and pancreatitis (n=19, 16.8%). There were no deaths. Conclusions In conclusion, history of hypertension, elevated INR above 1.2 and histology revealing HGD/cancer are potentially related to delayed post-ampullectomy bleeding. Moreover, time to delayed bleeding is 5.6 times faster in those with an INR≥1.2 only. These factors might be taken into consideration when strategizing a reduction in post-ampullectomy bleeding. Publication History Article published online: 15 April 2024 © 2024. European Society of Gastrointestinal Endoscopy. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany

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)0.002
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.337
Teacher spread0.314 · 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
Published2024
Admission routes1
Has abstractyes

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