Does Surgical Simulation Improve Hand-Sewn Bowel Anastomosis Skill Acquisition? A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Hand-sewn bowel anastomosis (HSBA) has been largely replaced by surgical stapler technology. However, HSBA remains an essential skill for general surgeons during technical stapler failures or for anatomic constraints. This systematic review evaluates the current evidence on the effectiveness of open and laparoscopic simulators in enhancing the HSBA skills of general surgery trainees and surgeons. Primary outcomes include technical performance score improvements and operative time reductions. Secondary outcomes include self-perceived skill acquisition and cost. METHODS: Following PRISMA guidelines, a peer-reviewed search strategy was conducted using MEDLINE, EMBASE, Scopus, and Cochrane. Two independent reviewers conducted the initial screen, yielding 30 studies. After full-text reviews, 15 studies were included in the final analysis. Study quality was evaluated using the Medical Education Research Quality Instrument, Oxford Center for Evidence-Based Medicine 2011 Levels, and the Grading of Recommendations, Assessment, Development, and Evaluations. RESULTS: Nine studies addressed open HSBA, and six explored laparoscopic HSBA. Eight of nine open simulators described improvement in technical scores, and three of four demonstrated an improvement in operative time when reported. All six laparoscopic simulators demonstrated an improvement in technical scores and three of four showed an improvement in operative time. Participants reported improved perceived skill acquisition with all simulators. Costs reported in six studies ranged from 0.84 USD to 3200 USD. Data quality ranged from medium to low. CONCLUSIONS: The use of open and lap HSBA simulators resulted in improved technical scores, operative time, and self-perceived competency. Further studies should be conducted to assess the transferability to in vivo HSBA.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".