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Record W1976684354 · doi:10.1002/rcs.99

Long‐distance robotic telesurgery: a feasibility study for care in remote environments

2006· article· en· W1976684354 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.

Bibliographic record

VenueInternational Journal of Medical Robotics and Computer Assisted Surgery · 2006
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsComputer scienceCadenceLatency (audio)Task (project management)Virtual realitySimulationReal-time computingPhysical medicine and rehabilitationMedicineArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

BACKGROUND: Basic telesurgical manoeuvres were conducted with signal delays. METHODS: Eight test subjects conducted four manoeuvres. Time delays of 0-1000 ms were investigated. Time to task completion and error rate were recorded in sequential delays of 0-600 ms. Additionally, blinded random delays of 0-1000 ms were studied. RESULTS: In the sequential trials (0-600 ms), there were no significant differences in average task time compared to zero latency. The error rate remained low despite increasing time delay, and was significantly less at 500 ms (p < 0.05). In the random trials, task time was significantly greater at delays of 500, 600, 800 and 1000 ms (p < 0.05). There were no significant differences in error rates (p = 0.252). CONCLUSIONS: Operators are capable of performing surgical exercises at significant delays. Latent video feedback is difficult for telesurgery. Visual or virtual reality cues should be implemented to aid the operator in a high-cadence telesurgery environment.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.450

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 head0.038
GPT teacher head0.326
Teacher spread0.288 · 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