Effects of network delay on training for telesurgery
Bibliographic record
Abstract
Telesurgery is defined as robotic surgery performed over a distance using a communication link. Methods of training for telesurgery are unknown. We investigated whether trainees perform better with latencies at the outset (700 ms) or with zero latency and a training ramp (0, 350, 700 ms). Four test exercises were conducted. Performance of the groups was compared for the different delay settings. Straight cut and grasp steadiness exercises showed no significant difference except at 700 ms versus 0 ms (p<0.05). In object placement, differences occurred in the lower latencies (700 ms versus 0 ms; 700 ms versus 350 ms; p<0.05), but the groups were not different at the end of the trial (700 ms versus 700 ms). A Fitts' law exercise showed no difference in performance between the groups at 700 ms. In conclusion, training for telesurgery may be equally effective with full latency at the outset or by using a training ramp.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".