Compression-Aided Millisecond-Scale Cauterization and its Application in Achieving Concurrent Hemostasis During Powered Tissue Resection
Bibliographic record
Abstract
Abstract Motorized tissue shavers are commonly used to rapidly fragment and remove intraventricular brain tumors during endoscopic neurosurgery. However, a key drawback of these devices is that they cannot be used on vascularized tumors as unmanageable levels of bleeding are encountered when they are fragmented. This problem could be addressed by concurrently applying a radiofrequency (RF) current to cauterize the tumor tissue as it is shaved, but this approach would require that cauterization occur in less than 50 ms. In this work, we investigated whether simultaneous tissue compression paired with a RF current can be used to achieve cauterization in millisecond timescales and then integrated this technique into the design of a combined cautery-shaver mechanism. We began by using both finite element modelling and benchtop experiments to demonstrate that cauterization occurs in timeframes as short as 15 ms if tissue is simultaneously compressed during RF current application. Based on these results, a novel shaver device that compresses and cauterizes tissue just prior to fragmentation was developed. Proof-of-concept prototypes demonstrated the design’s ability to both resect and rapidly cauterize tissue. Finally, we fabricated a fully mechanized version of the device that could execute a single tissue resection cycle on command. Testing on ex vivo brain samples showed that the tool successfully cauterized tissue to the depth necessary for hemostasis while simultaneously resecting a mass of tissue comparable to that removed by existing shaver tools.
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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 | 0.000 |
| 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".