P.042 The use of functional MRI in low grade glioma surgery - a Canadian survey
Bibliographic record
Abstract
Background: The goal of a brain tumour operation is maximal safe resection. No widely used guidelines for the use of functional magnetic resonance imaging (fMRI) in these patients currently exist. In this study we are trying to determine if and how Canadian neurosurgeons use fMRI in the management of patients with low grade glioma (LGG). Methods: A 15-25 minute survey was created using an online survey tool. In Part One of the study the survey was distributed to neurosurgery consultant and resident staff at the London Health Sciences Centre (LHSC). In Part Two of the study the survey is being distributed to members of the Canadian Neurosurgical Society. The survey consists of two sections - background and case-based decision making. Results: There were six surveys from the LHSC staff. On average respondents indicated that they obtain fMRI for 9% of LGG patients, though 67% indicated that they were comfortable ordering and interpreting fMRI studies. In the case-based section, fMRI data did not tend to affect respondents’ preferred treatment, confidence in their treatment, or their predicted risk of surgical treatment. Conclusions: In this limited survey of LHSC neurosurgical staff there was no regular use of fMRI in LGG patients. We await the results of a national survey of Canadian neurosurgeons.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".