MétaCan
Menu
Back to cohort
Record W2755935180 · doi:10.1055/s-0043-118095

Insertion of percutaneous endoscopic gastrostomy tubes with jejunal extensions using the “wedge” technique: a novel method to prevent retrograde tube migration into the stomach

2017· article· en· W2755935180 on OpenAlexaff
Michael Sey, H Gregor, Jamie Gregor, Brian Yan

Bibliographic record

VenueEndoscopy · 2017
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicinePercutaneous endoscopic gastrostomyWedge (geometry)GastrostomyStomachSurgeryTube (container)Gastrostomy tubePercutaneousEndoscopyInternal medicineMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Background and study aim In percutaneous endoscopic gastrostomy (PEG) with jejunal extension (PEGJ) procedures, retrograde migration of the jejunal extension tube into the stomach during endoscope withdrawal is a frustrating problem. We describe the novel “wedge” technique for inserting the jejunal extension tube, utilizing single-balloon enteroscopy to anchor it in place. Patients and methods Prospective 1-year study of consecutive patients undergoing PEGJ insertion at a single tertiary care center. The primary outcome was number of pyloric intubations required to place the jejunal extension tube. Secondary outcomes included success rate, time, and complications related to jejunal extension tube insertion. Results 17 patients underwent the procedure. The jejunal extension tube was inserted at the first attempt in 15 patients (88.2 %) and 2 required another pyloric intubation. Abdominal X-ray showed that all PEGJ tubes were successfully seated in the proximal jejunum. The mean (SD) time required for jejunal extension insertion was 16.9 (8.6) minutes. Two adverse events occurred due to PEG insertion although none were related to the jejunal extension insertion itself. Conclusions: The “wedge” technique is an effective and easy method for inserting a jejunal extension tube after PEG insertion.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.033
GPT teacher head0.362
Teacher spread0.330 · 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 designBench or experimental
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

Citations6
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueEndoscopySame topicClinical Nutrition and GastroenterologyFrench-language works237,207