The Queen’s closure: a novel technique for closure of endoscopic gastrotomy for natural-orifice transluminal endoscopic surgery
Bibliographic record
Abstract
BACKGROUND AND AIMS: Finding a reliable, safe, adaptable method of closing gastrotomies for natural-orifice transluminal endoscopic surgery (NOTES) procedures has been a major challenge facing this new clinical area. The Queen's NOTES Group has designed a novel endoscopic method of closing gastrotomies which involved using PolyLoop polyp ligature devices and endoscopic clips. The current study describes the technique and a pilot study of leak testing it versus hand-sewn suture closure. METHODS: Ten fresh pig stomachs were used, five for each technique. A 16-mm endoscopic gastrotomy was performed on the anterior wall of each. Five stomachs were then closed using the Queen's closure technique, and five with a hand-sewn double-layer suture technique. The stomachs were then connected to a water infusion device with sensitive pressure monitoring and were filled until leakage was detected at the closure site. RESULTS: The closures were all technically successful. The mean time for each gastrotomy and closure using the Queen's closure technique was 1.2 hours. The mean leak pressure for the Queen's closure was 51.8 mmHg and for the hand-sewn suture technique it was 80.8 mmHg ( P < 0.001). CONCLUSIONS: The Queen's closure technique holds promise as a reliable transferable technique for closing gastrotomies. Further study is necessary to evaluate its effects in live models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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.
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".