Transgastric and Transperineal Natural Orifice Translumenal Endoscopic Surgery (NOTES) in an Appendectomy Test Bed
Bibliographic record
Abstract
INTRODUCTION: Our purpose was to establish a NOTES appendectomy test bed to evaluate whether the transgastric or transperineal (transvaginal) approach is most efficient. METHODS: Using the uterine horns of female pigs as a model for appendectomy, 18 NOTES appendectomies were performed in 2 arms: 9 transgastric and 9 transvaginal. The primary outcome was mean total operative time for each technique excluding access closure. Secondary outcomes were peritoneal access and resection times. Means were compared using Student's t-test. RESULTS: Transgastric cases were faster than transperineal (46.5+/-14.5 vs 60.0+/-20.2 minutes, P=.02). Most of the improvement in transgastric times was due to faster resection (37.9+/-17.4 vs 51.3+/-16.5 minutes, P=.03). Neither approach was faster for peritoneal access (8.2+/-3.4 vs 8.3+/-4.5 minutes, nonsignificant). A significant learning curve was not demonstrated for the transgastric approach (53.0 vs 40.3 minutes, nonsignificant). A significant learning curve was demonstrated for the transperineal approach (76.0 vs 46.7 minutes, P=.02). Transperineal times improved over the study and approached transgastric; however, the last three transgastric cases were still significantly faster than the last three transperineal (40.3 vs 46.7 minutes, P=.02). No complications occurred in either group. CONCLUSIONS: The transgastric as compared with transperineal approach to NOTES appendectomy resulted in improved operative time in this model. The transperineal approach demonstrated a significant learning curve with operative times between techniques converging over time. This NOTES appendectomy test bed is suitable for evaluating NOTES innovations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".