Comparison of lateral thermal damage of the human peritoneum using monopolar diathermy, Harmonic scalpel and LigaSure
Bibliographic record
Abstract
BACKGROUND: New hemostatic technologies are often employed in open and laparoscopic surgery to reduce duration of surgery and complications. Monopolar diathermy, Harmonic scalpel and LigaSure are routinely used in open and laparoscopic surgery for tissue cutting and hemostasis. We compared lateral thermal damage following in vivo application of 3 commonly used instruments. METHODS: We used monopolar diathermy, Harmonic scalpel and LigaSure to coagulate and divide the peritoneum of patients who underwent median laparotomy. After anesthesia, median supraumbilical laparotomy was performed, and the peritoneum of each patient was coagulated using different devices. Using light microscopy and morphometric imaging analysis, the width of tissue lateral thermal damage was measured from the point of the peritoneal incision. RESULTS: We included 100 patients in our study. After a peritoneal incision, the mean lateral thermal damage of monopolar diathermy, Harmonic scalpel (output power 3), Harmonic scalpel (output power 5) and LigaSure were 215.79 μm, 90.42 μm, 127.48 μm and 144.18 μm, respectively. CONCLUSION: The degree of lateral thermal spread varied by instrument type, power setting and application time. LigaSure and Harmonic scalpel were the safest and most efficient methods of tissue coagulation. Monopolar diathermy resulted in the greatest degree of thermal damage in tissues.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".