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Record W4399266735 · doi:10.1002/rcs.2638

A haptic guidance system for simulated catheter navigation with different kinaesthetic feedback profiles

2024· article· en· W4399266735 on OpenAlex
Taha Abbasi‐Hashemi, Farrokh Janabi‐Sharifi, Asim N. Cheema, Kourosh Zareinia

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 · 2024
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsSt. Michael's HospitalToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHaptic technologyComputer scienceSimulationHuman–computer interaction

Abstract

fetched live from OpenAlex

BACKGROUND: This paper proposes a haptic guidance system to improve catheter navigation within a simulated environment. METHODS: Three force profiles were constructed to evaluate the system: collision prevention; centreline navigation; and a novel force profile of reinforcement learning (RL). All force profiles were evaluated from the left common iliac to the right atrium. RESULTS: Our findings show that providing haptic feedback improved surgical safety compared to visual-only feedback. If staying inside the vasculature is the priority, RL provides the safest option. It is also shown that the performance of each force profile varies in different anatomical regions. CONCLUSION: The implications of these findings are significant, as they hold the potential to improve how and when haptic feedback is applied for cardiovascular intervention.

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 score0.696
Threshold uncertainty score0.370

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 head0.016
GPT teacher head0.247
Teacher spread0.231 · 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