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Record W2809854563 · doi:10.1089/lap.2018.0126

Development of an Open-Source Laparoscopic Simulator Capable of Motion and Force Assessment: High Tech at Low Cost

2018· article· en· W2809854563 on OpenAlexaff
Bojan Gavrilović, Aodhnait S. Fahy, Brian Carrillo, Ahmed Nasr, J. Ted Gerstle, Georges Azzie

Bibliographic record

VenueJournal of Laparoendoscopic & Advanced Surgical Techniques · 2018
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of OttawaHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsSimulationComputer scienceMotion (physics)SoftwareInertial measurement unitOpen source hardwareMotion analysisLaptopFormative assessmentOpen sourceArtificial intelligence

Abstract

fetched live from OpenAlex

OBJECTIVE: Laparoscopic simulators help improve surgical skills in an ex vivo setting. New simulators incorporate force and motion assessment, but often at high financial cost. Our goal is to establish global access to a laparoscopic simulator, which offers both traditional summative assessment (time to task completion and precision) as well as advanced formative assessment (force and motion sensing capabilities) so that educators anywhere may be able to create simulators with increased educational value. DESIGN: A low-cost laparoscopic simulator incorporating an off-the-shelf optical sensor, inertial measurement unit, holders, and a housing unit for a microcontroller was integrated into a plastic box with a high-definition digital camera and a three-dimensional mouse. Open source software was developed to offer real-time feedback in force and motion. The system was calibrated for accuracy and consistency. RESULTS: -values of between 0.988 and 0.999. Accuracy in motion evaluation was evaluated and this demonstrated low average errors in the motion sensors of 5.4% to 6.8%. CONCLUSIONS: This low-cost, off-the-shelf, open-access laparoscopic simulator provides accurate and consistent measures of force and motion. We believe that collaborative efforts between surgeons and engineers can allow the creation of these surgical teaching devices at a reasonable cost such that they can be used in resource-rich and resource-limited settings.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.024
GPT teacher head0.354
Teacher spread0.330 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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