Evaluating bowel enterotomy closures in simulated deep body cavities using the reversing half-hitch alternating post and square knots: a randomized controlled trial
Bibliographic record
Abstract
Background: Square knots can be difficult to construct in deep body cavities. The reversing half-hitch alternating post (RHAP) surgical knot has noninferior tensile strength and performance characteristics in deep body cavities. We compared the enterotomy repairs of novice learners in simulated deep body cavities using RHAP versus square knots after proficiency-based training. Methods: Undergraduate students were randomized to RHAP (n = 10) or square knot (n = 10) groups and trained to defined proficiency. They then performed hand-sewn enterotomy repairs of cadaveric porcine small bowels on flat surfaces and in simulated deep body cavities. We recorded time to knot-tying proficiency and to enterotomy repair, and burst pressures for the repair. Results: Mean time-to-proficiency in knot tying was equivalent between the RHAP and square knot groups (23 [standard deviation (SD) 3] v. 21 [SD 2] min, p = 0.33). Mean time for enterotomy repair in deep cavities was shorter for the RHAP group (16 [SD 2] min v. 21 [SD 1] min, p = 0.02). Mean burst pressures for enterotomy repair were equivalent on flat surfaces (128 [SD 41] v. 101 [SD 36] mm Hg, p = 0.31), and were significantly higher for the RHAP group in simulated deep body cavities (32 [SD 13] v. 105 [SD 37] mm Hg, p = 0.05). Conclusion: The RHAP knots appear to have superior performance versus square knots when tied in a deep body cavity by novice learners. Future work should focus on demonstrating the clinical relevance and broad utility of the RHAP knot in abdominal surgery. Both knot types should be taught to novice learners.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".