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A Novel Bipolar Cautery Tool for Minimally-Invasive Neuroendoscopic Procedures

2020· article· en· W3081664282 on OpenAlexaff
Claudia Lutfallah, Thomas Looi, James M. Drake

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicThyroid and Parathyroid Surgery
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectrosurgeryForcepsBiomedical engineeringComputer scienceMinimally invasive proceduresSurgeryMedicine

Abstract

fetched live from OpenAlex

Electrosurgery is used in the operating room on a daily basis as a means to cut tissue and maintain hemostasis. The principle of this technology lies in the transfer of electricity from an electrosurgical unit to the operating site on a patient's body and modifying the waveform of that electricity to achieve the desired surgical effect. Bipolar cautery uses two electrodes, an active and a return, both at the surgical site to perform electrosurgery. Bipolar cautery can be very useful in helping surgeons to operate; however, current designs are not well suited to a 2.1 mm working channel in endoscopic procedures due to their rigid structure, limited range of motion, and bulky design. This paper describes a novel approach to designing a minimally- invasive bipolar cautery tool suitable for flexible neuroendoscopy. The system features 1.9 mm diameter bipolar tips which resemble grasping forceps, making it easier for surgeons to hold tissue while performing electrosurgery. The electrode wires also function as the actuating cables used to open and close the tips, which require 2.10 mm to open the tips to 30.9 °. The results show that the tool can safely cauterize a porcine brain specimen at various settings on the electrosurgical unit, and increasing the setting increases the area of tissue affected by the electricity. Repeatability was demonstrated and exhaustion was reached after the tool was opened and closed 73 times. Future work will involve improving the current design to increase the number of cycles the tool can survive before losing function.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.033
GPT teacher head0.266
Teacher spread0.233 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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