Infrared laser thermal fusion of blood vessels: preliminary<i>ex vivo</i>tissue studies
Bibliographic record
Abstract
Suture ligation of blood vessels during surgery can be time-consuming and skill-intensive. Energy-based, electrosurgical, and ultrasonic devices have recently replaced the use of sutures and mechanical clips (which leave foreign objects in the body) for many surgical procedures, providing rapid hemostasis during surgery. However, these devices have the potential to create an undesirably large collateral zone of thermal damage and tissue necrosis. We explore an alternative energy-based technology, infrared lasers, for rapid and precise thermal coagulation and fusion of the blood vessel walls. Seven near-infrared lasers (808, 980, 1075, 1470, 1550, 1850 to 1880, and 1908 nm) were tested during preliminary tissue studies. Studies were performed using fresh porcine renal vessels, ex vivo, with native diameters of 1 to 6 mm, and vessel walls flattened to a total thickness of 0.4 mm. A linear beam profile was applied normal to the vessel for narrow, full-width thermal coagulation. The laser irradiation time was 5 s. Vessel burst pressure measurements were used to determine seal strength. The 1470 nm laser wavelength demonstrated the capability of sealing a wide range of blood vessels from 1 to 6 mm diameter with burst strengths of 578 ± 154, 530 ± 171, and 426 ± 174 mmHg for small, medium, and large vessel diameters, respectively. Lateral thermal coagulation zones (including the seal) measured 1.0 ± 0.4 mm on vessels sealed at this wavelength. Other laser wavelengths (1550, 1850 to 1880, and 1908 nm) were also capable of sealing vessels, but were limited by lower vessel seal pressures, excessive charring, and/or limited power output preventing treatment of large vessels (>4 mm outer diameter).
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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 | 0.000 | 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".