P.094 Incorporating Navigated Transcranial Magnetic Stimulation (nTMS) into the neurosurgical practice: oncological, vascular and research potentials
Bibliographic record
Abstract
Background: Surgical managment of eloquent lesions in the brain require a multidisciplinary approach. Radiographic imaging, such as magnetic resosnance, can provide details of “normal” anatomy however are limited when lesions can distort/displace due to mass effect or neuroplasticity. Functional MRI (fMRI) has limitations due to patient dependent actions can often be limited due oncological or vascular lesions but known to still be near or involving “eloquent” cortex. Navigated transcranial magnetic stimulation (nTMS) provides the physician with the ability to accurately (~2mm error) stimulate cortex of the brain, in a clinical setting, and to understand function of areas of motor and language and incorporate this information into the surgical theatre. Methods: We will present a personal expierience of complex oncological and vascular cases to illustrate how nTMS can assist in the determination of surgical approaches and educating patients of potential morbidities. Will also review potential research opportunities that nTMS provides. Details of Phase 2 clinical trial of nTMS for improving neuro-cognitive outcomes will be discussed. Results: Case ilustrations will be provided. Preliminary results of Phase 2 clinical study will be discussed. Conclusions: Navigated TMS provides another tool in the armamentarium of neurosurgeons to better manage and approach complex and eloquent lesions in the brain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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".