NIMG-46THE USE OF CLINICAL FUNCTIONAL MAGNETIC RESONANCE IMAGING IN BRAIN TUMOUR PATIENTS FOR SURGICAL PLANNING: A SINGLE-SURGEON RETROSPECTIVE COHORT STUDY AT A CANADIAN CENTER
Bibliographic record
Abstract
BACKGROUND: Advanced imaging techniques such as functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) are being increasingly used for the pre-operative evaluation of patients with brain tumors. METHODS: The study is a retrospective chart review investigating the use of fMRI from 2002-2013 in the pre-operative evaluation of brain tumour patients. Baseline demographic and clinical data were collected. The specific fMRI protocols used for each patient were recorded. RESULTS: 60 patients were identified over the twelve-year period. The type of tumours most commonly investigated were low-grade glioma, glioblastoma multiforme and meningioma. Most common presenting concerns were seizures (46%), change on surveillance (15%), language deficits (11%), and headache (10%). There was a predominance of left-hemispheric lesions investigated with fMRI (77.6% versus 22.4% for right). The most commonly involved lobes were frontal (46.5%), temporal (28.8%), parietal (16.4%), then insular (5.5%). The most common fMRI paradigms were language (85.2%), motor (79.6%), sensory (18.5%), and memory (9.3%). Most patients ultimately underwent a craniotomy (73.2%) while smaller groups underwent stereotactic biopsy (10.7%) and non-surgical management (16.1%). Time from request for fMRI to actual fMRI acquisition was 3.2 +/− 2.3 weeks. Time from fMRI acquisition to intervention was 5.2 +/− 5.4 weeks. CONCLUSIONS: We have characterized patient demographics in a retrospective single-surgeon cohort undergoing pre-operative clinical fMRI at a Canadian center. Our experience suggests an acceptable wait time from scan request to scan completion/analysis and from scan to intervention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".