A New Laparoscopic Morcellator Using an Actuated Wire Mesh and Bag
Bibliographic record
Abstract
Abstract Laparoscopic morcellation is a technique used in gynecological surgeries such as hysterectomy and myomectomy to remove uteri and uterine fibroids (leiomyomas) through a small abdominal incision. Current morcellators use blades or bipolar energy to cut tissue into small pieces that are then removed through laparoscopic ports in a piecewise manner. These existing approaches have several limitations; (1) they are time consuming as the tissue must be manually moved over the devices during the cutting step and removal is piecewise, (2) they can lead to accidental damage to surrounding healthy tissue inside the body and (3) they do not provide safe containment of tissue during the morcellation process which can lead to seeding (spreading and regrowth) of benign or potentially cancerous tissue. This paper describes a laparoscopic morcellator that overcomes these limitations through a new design that is based on an enclosed, motor-actuated mesh that applies only an inward-directed cutting force to the tissue after it has been loaded into the protective mesh and bag. The deterministic design approach that led to this concept is presented along with the detailed electromechanical design. The prototype is tested on soft vegetables and an animal model to demonstrate successful morcellation and how the device would be compatible with current clinical practice. Results show that the time required to morcellate with the new device for a set of tests on animal tissue is relatively uniform across samples with widely varying parameters. Including tissue manipulation and extraction time, the new device is shown to have an improvement in terms of speed over current morcellators. The mean time for cutting animal tissue ranging from 100 g to 360 g was 30 s with small variations due to initial conditions. The time for cutting is expected to remain approximately constant as tissue size increases. There is also minimal risk of the protective bag ripping due to the inward-cutting action of the mesh, thereby potentially significantly reducing the risk of seeding during clinical procedures; thus, further increasing patient safety. Finally, this design may be applicable to other procedures involving removal of tissue in nongynecologic surgeries, such as full or partial kidney or spleen removal.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".