Insertion of percutaneous endoscopic gastrostomy tubes with jejunal extensions using the “wedge” technique: a novel method to prevent retrograde tube migration into the stomach
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".