Long‐distance robotic telesurgery: a feasibility study for care in remote environments
Bibliographic record
Abstract
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".