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Record W2134402458 · doi:10.1109/biorob.2008.4762804

Design of a sensorized instrument for skills assessment and training in minimally invasive surgery

2008· article· en· W2134402458 on OpenAlexaff
Ana Luisa Trejos, Rajni V. Patel, Michael D. Naish, Christopher M. Schlachta

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsLawson Health Research Institute
Fundersnot available
KeywordsInvasive surgerySurgical instrumentComputer scienceTorqueDegrees of freedom (physics and chemistry)Strain gaugePosition (finance)SimulationMeasure (data warehouse)Medical physicsArtificial intelligenceHuman–computer interactionSurgeryEngineeringMedicinePhysics

Abstract

fetched live from OpenAlex

The restricted access conditions during minimally invasive surgery (MIS) result in perceptual-motor relationships that are unfamiliar to the novice surgeon and require training to overcome. To aid in MIS skills assessment and training, a novel sensorized instrument has been designed. Strain gauges attached to the instrument measure forces and torques acting at its tip, corresponding to all 5 degrees of freedom (DOFs) available during MIS. A position tracker provides tip motion feedback in 6 DOFs. The instrument is similar in shape, size and weight to traditional laparoscopic instruments, allowing it to be used in any MIS training environment. Furthermore, replaceable tips and handles make the instruments highly versatile. The results of the experimental evaluation show that there are clear differences in both the force and position profiles of trainees and surgeons with different levels of experience.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.117
GPT teacher head0.333
Teacher spread0.216 · 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 designObservational
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

Citations29
Published2008
Admission routes1
Has abstractyes

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