Tissue Stress from Laparoscopic Grasper Use and Bowel Injury in Humans: Establishing Intraoperative Force Boundaries
Bibliographic record
Abstract
Abstract Background Inappropriate force in laparoscopic surgery can lead to inadvertent tissue injury. It is currently unknown however at what magnitude of compressive stress trauma occurs in gastrointestinal tissues. Methods This study included 10 small bowel and 10 colon samples. Each was compressed with pressures ranging from 100 kPa to 600 kPa by a novel device to induce compressive “grasps” to simulate those of a laparoscopic grasper. Experimentation was performed ex-vivo, in-vitro . Grasp conditions of 0 to 600 kPa for a duration of 10 seconds were utilized. Two pathologists who were blinded to all study conditions, performed a histological analysis of the tissues. Patients were eligible if their surgery procured healthy tissue margins for experimentation (a convenience sample). 26 patient samples were procured; six samples were unusable. 10 colon and 10 small bowel samples were tested for a total of 120 experimental cases. No patients withdrew their consent. Two metrics of damage were quantified: an intestinal layer thickness calculation where the serosa layer was measured in the area of compression and compared to a local control and a histological scoring scale for tissue trauma. Results Small bowel (10), M:F was 7:3, average age was 54.3 years. Colon (10), M:F was 1:1, average age was 65.2 years. All 20 patients experienced a significant difference (p<0.05) in serosal thickness post-compression at both 500 and 600 kPa for both tissue types. A logistic regression analysis with a sensitivity of 100% and a specificity of 84.6% on a test set of data predicts a safety threshold of 329-330 kPa. Conclusion A threshold was discovered that corresponded to both significant serosal thickness change and a positive histological trauma score rating. This “force limit” could be used in novel sensorized laparoscopic tools to avoid intraoperative tissue injury.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".