Video Microscope Robotic Arm-Assisted, Neuronavigation-guided Glioma Resection and Regional Sampling
Bibliographic record
Abstract
High-grade gliomas possess internal pathological heterogeneity. Selective sampling of different tumor regions can help in the study of this heterogeneity. In this report, we have described the use of a novel navigation and optical system for the selective regional sampling of a high-grade glioma lesion. A 45-year-old gentleman presented to us with complaints of intermittent frontal headaches for past eight months. On examination, he had subtle pyramidal weakness in left upper and lower extremities. Magnetic resonance imaging (MRI) showed a large contrast-enhancing, space-occupying lesion in the right frontal lobe causing perilesional edema and midline shift. We marked four different regions on the preoperative MRI using apparent diffusion coefficient (ADC) mapping and contrast enhancement pattern in four different combinations using presurgical planning software (BrightMatter™ Plan) (Synaptive Medical, Inc., Toronto, Canada). These pre-identified areas were exported into BrightMatter™ Servo (Synaptive Medical, Toronto, Canada), an integrated robotic video microscope with a neuronavigation system where these areas were selectively sampled and sent for analysis. The BrightMatter™ Servo not only helped us to the target areas but also helped to identify a safe trajectory, respecting white matter tracts. Histopathology showed a neoplastic lesion composed of mononuclear round cells with the perinuclear halo in a fibrillary stroma with admixed mini-gemistocytes consistent with the diagnosis of a Grade 3 anaplastic astrocytoma. A selective regional sampling of the gliomas can be reliably performed using BrightMatter™ technologies to study the pathological heterogeneity of these lesions.
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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 | 0.000 | 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) | 0.001 | 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".