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Record W2020774722 · doi:10.1002/rcs.269

Teleoperated master–slave needle insertion

2009· article· en· W2020774722 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Medical Robotics and Computer Assisted Surgery · 2009
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTeleoperationHaptic technologyComputer scienceRoboticsDeflection (physics)SimulationSoft roboticsArtificial intelligenceRobot

Abstract

fetched live from OpenAlex

BACKGROUND: Accuracy of needle tip placement and needle tracking in soft tissue are of particular importance in many medical procedures. In recent years, developing autonomous and teleoperated systems for needle insertion has become an active area of research. METHODS: In this study, needle insertion was performed using a master-slave set-up with multi-degrees of freedom. The effect of force feedback on the accuracy of needle insertion was investigated. In addition, this study compared autonomous, teleoperated and semi-autonomous needle insertion. RESULTS: The results of this study show that incorporation of force feedback can improve teleoperated needle insertion. However, autonomous and semi-autonomous needle insertions, which use feedback from a deflection model, provide significantly better performance. CONCLUSIONS: Development of a haptic master-slave needle insertion system, which is capable of performing some autonomous tasks based on feedback from tissue deformation and needle deflection models, can improve the performance of autonomous robotics-based insertions as well as non-autonomous teleoperated manual insertions.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.861
Threshold uncertainty score0.380

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 head0.023
GPT teacher head0.250
Teacher spread0.227 · 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