Defining the Relationship Between Compressive Stress and Tissue Trauma During Laparoscopic Surgery Using Human Large Intestine
Bibliographic record
Abstract
Excessive magnitudes of compressive stress exerted on gastrointestinal tissues can lead to pathological scar tissue or adhesion formation, bleeding, inflammation or even death from bowel perforation and sepsis. It is currently unknown however, at exactly what magnitude of compressive stress that these pathologies occur. A novel simple compressive device was engineered to provide an objective means of producing discrete compressive stresses on human tissues. Samples of human large intestine (colon) were removed from consenting patients as a part of their standard surgical procedure. These samples were compressed with a range of loads normally produced by standard laparoscopic graspers in representative abdominal surgeries. After compression, specimens were processed for histological analysis and assessed. The two independent pathologists who were blinded to stress magnitudes were both able to quantify increasing tissue damage that corresponded to increasing amounts of compressive force. A threshold between 350-450 kPa was discovered that corresponded to both significant serosal thickness change and a positive histological trauma score rating. Whether the tissue injury quantified is pathologic is subject for future in-vivo longitudinal investigation but certainly based on literature, can be the basis of pathological adhesion formation or an area for hemorrhage and scar formation.
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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 | 0.000 |
| 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".