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Record W1997788618 · doi:10.1115/1.4026294

A New Laparoscopic Morcellator Using an Actuated Wire Mesh and Bag

2013· article· en· W1997788618 on OpenAlex
Alexander Isakov, Kimberly M. Murdaugh, William C. Burke, Sloan Zimmerman, Ellen T. Roche, Dónal Holland, Jon I. Einarsson, Conor J. Walsh

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Medical Devices · 2013
Typearticle
Languageen
FieldMedicine
TopicMinimally Invasive Surgical Techniques
Canadian institutionsTrinity College
FundersHansjörg Wyss Institute for Biologically Inspired Engineering, Harvard UniversityHarvard School of Engineering and Applied SciencesNational Science Foundation
KeywordsProcess (computing)Computer scienceSurgeryBiomedical engineeringMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.041
GPT teacher head0.340
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it