Robotics in the neurosurgical treatment of glioma
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.
Bibliographic record
Abstract
BACKGROUND: The treatment of glioma remains a significant challenge with high recurrence rates, morbidity, and mortality. Merging image guided robotic technology with microsurgery adds a new dimension as they relate to surgical ergonomics, patient safety, precision, and accuracy. METHODS: An image-guided robot, called neuroArm, has been integrated into the neurosurgical operating room, and used to augment the surgical treatment of glioma in 18 patients. A case study illustrates the specialized technical features of a teleoperated robotic system that could well enhance the performance of surgery. Furthermore, unique positional and force information of the bipolar forceps during surgery were recorded and analyzed. RESULTS: The workspace of the bipolar forceps in this robot-assisted glioma resection was found to be 25 × 50 × 50 mm. Maximum values of the force components were 1.37, 1.84, and 2.01 N along x, y, and z axes, respectively. The maximum total force was 2.45 N. The results indicate that the majority of the applied forces were less than 0.6 N. CONCLUSION: Robotic surgical systems can potentially increase safety and performance of surgical operation via novel features such as virtual fixtures, augmented force feedback, and haptic high-force warning system. The case study using neuroArm robot to resect a glioma, for the first time, showed the positional information of surgeon's hand movement and tool-tissue interaction forces.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it