P10.18 The impact of clinical functional MRI on surgical decision making in low grade glioma - a survey of Canadian neurosurgeons
Bibliographic record
Abstract
BACKGROUND: The goal of brain tumour resection is maximal removal of unhealthy tissue with minimal disruption of neurological function. For tumours in eloquent cortical regions, the balance between maximal tumour resection and preservation of functional tissue is exceedingly delicate. The tools available to surgeons to determine patients’ functional anatomy tend to be invasive. Functional magnetic resonance imaging (fMRI) is a non-invasive, and increasingly popular tool for functional mapping in neurosurgical patients. However, no widely used guidelines for the use of fMRI in neurosurgical patients currently exist. In this study, we are conducting a survey of Canadian neurosurgeons to elucidate how they use fMRI, and how it impacts their approach to patients with intracranial low grade glioma (LGG). METHODS: A 15-25 minute survey was created using an online survey tool. It has been distributed to neurosurgery staff and trainees at the London Health Sciences Centre (LHSC) through email, and in the future will be distributed to members of the Canadian Neurosurgical Society (CNS). The survey consists of two sections - background, and case-based decision making. In the background section, respondents are asked questions related to their practice, typical approach to LGG, and use of fMRI. In the case-based section, five cases of patients with LGG who underwent preoperative fMRI are presented. Initially structural MRI and clinical details of each case are provided, and respondents are asked about their preferred management, confidence in their preferred treatment, and predicted risk of surgical complications. Subsequently, respondents are also presented with fMRI details of each case, and asked an identical series of questions. Results: We collected 6 surveys of 24 distributed to neurosurgery staff and trainees at LHSC. On average, respondents stated they order preoperative fMRI for 9% of brain tumour patients. 67% of respondents stated they are comfortable ordering and interpreting fMRI for brain tumour patients, 17% stated they are not comfortable, and 17% stated they are neither comfortable nor uncomfortable. No respondents expressed a preference for early surgical intervention for LGG patients, 50% prefer watchful waiting, and 50% expressed no preference. 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 complications. We expect to collect 59-147 completed national surveys, based on a predicted 20-50% response rate of 293 CNS members. Conclusions: We expect this study to elucidate how Canadian neurosurgeons use fMRI in practice, and if fMRI allows them to recommend more aggressive surgical treatment for LGG patients with greater confidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".