Toward Robot-Assisted Neurosurgical Lasers
Bibliographic record
Abstract
Despite the potential increase in precision and accuracy, laser technology is not widely used in neurological surgery. This in part relates to challenges associated with the early introduction of lasers into neurosurgery. Considerable advances in laser technology have occurred, which together with robotic technology could create an ideal platform for neurosurgical application. In this study, a 980-nm contact diode laser was integrated with neuroArm. Preclinical evaluation involved partial hepatectomy, bilateral nephrectomy, splenectomy, and bilateral submandibular gland excision in a Sprague-Dawley rat model (n = 50). Total surgical time, blood loss as weight of surgical gauze before and after the procedure, and the incidence of thermal, vascular, or lethal injury were recorded and converted to an overall performance score. Thermal damage was evaluated in the liver using tissue samples stained with hematoxylin and eosin. Clinical studies involved step-wise integration of the 980-nm laser system into four neurosurgical cases. Results demonstrate the successful integration of contact laser technology into microsurgery, with and without robotic assistance. In preclinical studies, the laser improved microsurgical performance and reduced thermal damage, while neuroArm decreased intra- and intersurgeon variability. Clinical studies demonstrate dutility in meningioma resection (n = 4). Together, laser and robotic technology offered a more consistent, expedient, and precise tool for microsurgery.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".