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Record W2163880280 · doi:10.3109/10929080500228654

The impact of latency on surgical precision and task completion during robotic-assisted remote telepresence surgery

2005· article· en· W2163880280 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

VenueComputer Aided Surgery · 2005
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsSt. Joseph’s Healthcare HamiltonBell (Canada)McMaster University
Fundersnot available
KeywordsLatency (audio)Computer scienceTask (project management)EngineeringTelecommunications

Abstract

fetched live from OpenAlex

OBJECTIVE: It has been suggested that robotic-assisted remote telepresence surgery with a signal transmission latency of greater than 300 ms may not be possible. METHODS: We evaluated the impact of four different latencies of up to 500 ms on task completion and error rate in five surgeons after completion of three different surgical tasks. RESULTS: The surgeons were able to complete all tasks with a latency of 500 ms. However, higher latency was associated with higher error rates and task completion time (TCT). There were significant variations between surgeons and different tasks. CONCLUSION: Surgeons are able to complete tasks with a signal transmission latency of up to 500 ms. The clinical impact of slower TCT and increased error rates encountered at higher latency needs to be established.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score0.524

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 head0.047
GPT teacher head0.314
Teacher spread0.267 · 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