Evaluation of Teleoperated Surgical Robots in an Enclosed Undersea Environment
Bibliographic record
Abstract
The ability to support surgical care in an extreme environment is a significant issue for both military medicine and space medicine. Telemanipulation systems, those that can be remotely operated from a distant site, have been used extensively by the National Aeronautics and Space Administration (NASA) for a number of years. These systems, often called telerobots, have successfully been applied to surgical interventions. A further extension is to operate these robotic systems over data communication networks where robotic slave and master are separated by a great distance. NASA utilizes the National Oceanographic and Atmospheric Administration (NOAA) Aquarius underwater habitat as an analog environment for research and technology evaluation missions, known as NASA Extreme Environment Mission Operations (NEEMO). Three NEEMO missions have provided an opportunity to evaluate teleoperated surgical robotics by astronauts and surgeons. Three robotic systems were deployed to the habitat for evaluation during NEEMO 7, 9, and 12. These systems were linked via a telecommunications link to various sites for remote manipulation. Researchers in the habitat conducted a variety of tests to evaluate performance and applicability in extreme environments. Over three different NEEMO missions, components of the Automated Endoscopic System for Optimal Positioning (AESOP), the M7 Surgical System, and the RAVEN were deployed and evaluated. A number of factors were evaluated, including communication latency and semiautonomous functions. The M7 was modified to permit a remote surgeon the ability to insert a needle into simulated tissue with ultrasound guidance, resulting in the world's first semi-autonomous supervisory-controlled medical task. The deployment and operation of teleoperated surgical systems and semi-autonomous, supervisory-controlled tasks were successfully conducted.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".