A Novel Liver Parenchyma Transection Technique Using Locking Straight Rigid Ties. An Experimental Study in Pigs
Bibliographic record
Abstract
INTRODUCTION: Technological advances have led to the development of many devices used in liver resections. However, no single transection tool is uniformly considered to be better than the others. This study aimed to develop an effective, fast, and cost-efficient technique for hepatic parenchymal transection. MATERIALS AND METHODS: A liver parenchyma compression device in the form of a locking straight rigid tie (LoStRiT) was newly developed. Twelve pigs were distributed into two groups. The control group ( n = 6) comprised animals that underwent hepatectomy using the standard Kelly-clysis technique. The study group (n = 6) comprised animals that underwent hepatectomy using sequential LoStRiT mechanisms. The transection speed, blood loss, and biloma formation were recorded. RESULTS: The mean parenchymal transection speed was 1.27 ± 0.27 cm(2)/min for the control group and 2.39 ± 0.56 cm(2)/min for the LoStRiT group ( p = .003). The mean blood loss per kilogram of body weight was 9.8 ± 5.2 ml/kg for the control group and 3.9 ± 0.9 ml/kg for the LoStRiT group ( p = .040). No bilomas were identified. CONCLUSION: LoStRiT hepatectomy appears to be effective, fast, and reproducible in a porcine model of liver resection. Further development of this novel and potentially cost-efficient technique includes construction of the device using absorbable materials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".