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Record W4367189163 · doi:10.7759/cureus.38152

Multiple Cholecystoenteric Fistulae With Bouveret Syndrome and Acute Pancreatitis: A Rare Combination

2023· article· en· W4367189163 on OpenAlexaff
Dattaprasanna Kulkarni, Pooja P Goradia, Neha D Kulkarni, Shrikant Garge

Bibliographic record

VenueCureus · 2023
Typearticle
Languageen
FieldMedicine
TopicBiliary and Gastrointestinal Fistulas
Canadian institutionsPancreas Centre (Canada)
Fundersnot available
KeywordsMedicineGallstone ileusAcute pancreatitisFistulaPancreatitisEndoscopyLaparoscopySurgeryIleusGallstonesRadiologyGeneral surgeryLaparoscopic surgery

Abstract

fetched live from OpenAlex

Multiple cholecystoenteric fistulae, Bouveret syndrome (a form of gallstone ileus), and acute pancreatitis occurring together is very rare. Diagnosis is seldom clinical and is mostly based on computerised tomography (CT) or magnetic resonance imaging (MRI). Endoscopy and minimally invasive surgery have revolutionised the treatment of Bouveret syndrome and cholecystoenteric fistula, respectively, over the last two decades. Laparoscopic repair of cholecystoenteric fistula followed by cholecystectomy is successful on a consistent basis with skilled laparoscopic suturing and advanced laparoscopy. In patients with Bouveret syndrome, when the stone is <4cm and is in the proximal duodenum, it is usually amenable for endoscopic extraction with snares, nets, forceps and lithotripsy. When endoscopy is unavailable or fails, laparoscopic surgery is suitable for these patients. However, stones >4 cm, located in the distal duodenum, multiple fistulae, and associated acute pancreatitis may necessitate open surgery. We present here a case of a 65-year-old Indian female with multiple cholecystoenteric fistulae and Bouveret syndrome with acute pancreatitis with a 6.5 cm gallstone diagnosed on CT scan and MRI and treated successfully by open surgery. We also review the current literature on the management of this complex problem.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.011
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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 teacher head, 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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