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P0739 PERCUTANEOUS ENDOSCOPIC GASTROSTOMY TUBES (PEG) IN CHILDREN: A RARE COMPLICATION

2004· article· en· W2025058462 on OpenAlexaff
Stefano Giuliani, Pietro Betalli, Paola Midrio, G. Mognato, Piergiorgio Gamba

Bibliographic record

VenueJournal of Pediatric Gastroenterology and Nutrition · 2004
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsPediatric Oncology Group
Fundersnot available
KeywordsMedicinePercutaneous endoscopic gastrostomySurgeryPerforationGastrostomyComplicationPEG ratioEsophagus

Abstract

fetched live from OpenAlex

Introduction: Percutaneous endoscopic gastrostomy (PEG) has been widely accepted as an efficacious way of nutritional support in infant and child. Scanty information have been reported regarding frequency and type of complications, arising from removal of PEG tubes in these patients. The aim of the report is to describe three pediatric cases of retained internal bumper after PEG. Methods: The records of 102 patients who had PEG tubes placed from January 1992 to July 2003 were reviewed for complications after removal. In our centre approximately 3 months after the procedure all catheters were removed and replaced with gastric buttons or tubes. Because it can be painful we have always performed this procedure in theatre under sedation. Results: Breaking of the bumper during tube removal by traction occurred in three patients (2.9%). In two, the foreign body was endoscopically removed. In one patient the bumper migrated in the esophagus and caused perforation of the esophageal wall with important mediastinitis. Conclusion: We suggest to remove the PEG always in theatre, with endoscopic control, to minimize child discomfort, to manage and to avoid possible serious complications. The retained bumper must be immediately removed.

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)0.007
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.260
Teacher spread0.251 · 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

Citations1
Published2004
Admission routes1
Has abstractyes

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