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Record W2481947906 · doi:10.14740/jmc.v7i8.2569

Buried Bumper Syndrome: A Case Report of a Rare Complication of Percutaneous Endoscopic Gastrostomy

2016· article· en· W2481947906 on OpenAlexvenueno aff
Daniel Paramythiotis, Konstantinia Kofina, Vassileios Papadopoulos, Antonios Michalopoulos

Bibliographic record

VenueJournal of Medical Cases · 2016
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePercutaneous endoscopic gastrostomySurgeryComplicationGastrostomySwallowingStomachAbdominal wallDebridement (dental)PEG ratioInternal medicine

Abstract

fetched live from OpenAlex

Percutaneous endoscopic gastrostomy (PEG) is thought to be a, relatively, safe procedure with a low rate of complications. Buried bumper syndrome (BBS) is the migration of the internal fixation device of PEG (bumper) out of the stomach and consists of a major and, usually, late complication with potentially lethal results. We present a case of such complication resulting in an extended anterior abdominal wall necrosis. An 87-year-old woman with a PEG placement 6 months before, due to Alzheimer’s disease and inability of oral feeding, presented in a severe septic condition and with necrotic inflammation of the abdominal wall. Computerized tomography confirmed the migration of internal bumper subcutaneously. Excision of the gastrostomy and surgical debridement was performed, but, due to the patient’s deteriorated condition, she died after 24 hours. PEG is ideal for patients with swallowing deficiencies, but severe complications may occur. Prevention and initiate diagnosis of the BBS are important for an early treatment, in order to avoid such severe complications. J Med Cases. 2016;7(8):331-333 doi: http://dx.doi.org/10.14740/jmc2569w

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)0.000
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.003
Science and technology studies0.0030.003
Scholarly communication0.0020.003
Open science0.0020.003
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.0020.001

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 head0.034
GPT teacher head0.340
Teacher spread0.306 · 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 designCase report
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
Published2016
Admission routes1
Has abstractyes

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