The Evolution of neuroArm
Bibliographic record
Abstract
Intraoperative imaging disrupts the rhythm of surgery despite providing an excellent opportunity for surgical monitoring and assessment. To allow surgery within real-time images, neuroArm, a teleoperated surgical robotic system, was conceptualized. The objective was to design and manufacture a magnetic resonance-compatible robot with a human-machine interface that could reproduce some of the sight, sound, and touch of surgery at a remote workstation. University of Calgary researchers worked with MacDonald, Dettwiler and Associates engineers to produce a requirements document, preliminary design review, and critical design review, followed by the manufacture, preclinical testing, and clinical integration of neuroArm. During the preliminary design review, the scope of the neuroArm project changed to performing microsurgery outside the magnet and stereotaxy inside the bore. neuroArm was successfully manufactured and installed in an intraoperative magnetic resonance imaging operating room. neuroArm was clinically integrated into 35 cases in a graded fashion. As a result of this experience, neuroArm II is in development, and advances in technology will allow microsurgery within the bore of the magnet. neuroArm represents a successful interdisciplinary collaboration. It has positive implications for the future of robotic technology in neurosurgery in that the precision and accuracy of robots will continue to augment human capability.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".