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Record W2408266754 · doi:10.3233/978-1-58603-964-6-233

Development of a Virtual Reality Haptic Veress Needle Insertion Simulator for Surgical Skills Training

2009· article· en· W2408266754 on OpenAlexaff
Allan Okrainec, Mónica Farcas, Óscar Henao, Ian Choy, J Green, Mahyar Fotoohi, R. Leslie, Donal R. Wight, Paul A. Karam, Nora Argelia Aguilera González, Jacob Apkarian

Bibliographic record

VenueStudies in health technology and informatics · 2009
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsVeress needleHaptic technologyVirtual realitySimulationPneumoperitoneumMedicineLaparoscopyComputer scienceHuman–computer interactionMedical physicsSurgery

Abstract

fetched live from OpenAlex

The Veress needle is the most commonly used technique for creating the pneumoperitoneum at the start of a laparoscopic surgical procedure. Inserting the Veress needle correctly is crucial since errors can cause significant harm to patients. Unfortunately, this technique can be difficult to teach since surgeons rely heavily on tactile feedback while advancing the needle through the various layers of the abdominal wall. This critical step in laparoscopy, therefore, can be challenging for novice trainees to learn without adequate opportunities to practice in a safe environment with no risk of injury to patients. To address this issue, we have successfully developed a prototype of a virtual reality haptic needle insertion simulator using the tactile feedback of 22 surgeons to set realistic haptic parameters. A survey of these surgeons concluded that our device appeared and felt realistic, and could potentially be a useful tool for teaching the proper technique of Veress needle insertion.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.908
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.106
GPT teacher head0.412
Teacher spread0.307 · 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 designOther design
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

Citations17
Published2009
Admission routes1
Has abstractyes

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