Pre-Surgical and Surgical Planning in Neurosurgical Oncology - A Case-Based Approach to Maximal Safe Surgical Resection in Neurosurgery
Bibliographic record
Abstract
Use of functional neuroimaging capabilities such as fMRI, DTI, MRP, MRS, AS-PET-CT, SPECT, and TMS as noninvasive tools to visualize intrinsic brain and spine morphology in relation to function have developed over the past 30 years. Amongst these imaging modalities, functional magnetic resonance imaging (fMRI) is of particular interest since it follows the physiological coupling between neuronal electrical activity and metabolic structural (cellular) activity as it relates to tissue vascularity and perfusion states. This structure–function synesis (from the Greek noun, σύνεσις = being together), leads to three effects that contribute to the fMRI signal: an increase in the blood flow velocity, a change in the mean blood volume, and most importantly, alterations in the blood oxygenation level. The latter effect has lent to the development of blood-oxygenation-level-dependent or BOLD fMRI, which has been used in establishing the topographic relationship between eloquent cortex and neurosurgical planning. As an adjunct to this modality, MRI-based diffusion tensor imaging (DTI) allows further detailed radiographic assessment of fiber tracts in the brain in relationship to the surgical lesion of interest. Herein we review the roles of fMRI and DTI for presurgical mapping to allow for maximal safe resection procedures in neurosurgery with case-based illustrations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".