Awake Craniotomy Under 3-Tesla Intraoperative Magnetic Resonance Imaging: A Retrospective Descriptive Report and Canadian Institutional Experience
Bibliographic record
Abstract
Background: The role of high-field 3-Tesla intraoperative magnetic resonance imaging (I-MRI) during awake craniotomy (AC) has not been extensively studied. We report the feasibility and safety of AC during 3-Tesla I-MRI. Methods: This retrospective descriptive report compared 3 groups: AC with minimal sedation and I-MRI; I-MRI-guided craniotomy under general anesthesia (GA), and; AC without I-MRI. Perioperative factors, surgical, anesthetic and radiologic complications, and postoperative morbidity and mortality were recorded. Results: Overall, 85 patients are included in this report. Five of 23 patients (22%) who underwent AC with I-MRI had anesthetic complications (nausea/vomiting and conversion to GA) compared with 3 of 40 (8%) who underwent I-MRI-guided craniotomy under GA (nausea/vomiting during extubation, and arrhythmia). Intraoperative surgical complications (seizures and speech deficits) occurred in 5 patients (22%) who underwent AC and I-MRI, excessive intraoperative bleeding occurred in 2 patients (5%) who had I-MRI-guided craniotomy under GA, and 4 of 22 (18%) patients who underwent AC without I-MRI experienced neurological complications (seizures, motor deficits, and transient loss of consciousness). Eight patients (20%) who had I-MRI with GA had postoperative complications, largely neurological. The duration of surgery and anesthesia were shortest in the group of patients receiving AC without I-MRI. Seventy-three percent of the patients in this group had residual tumor postoperatively compared with 44% and 38% in those having I-MRI with AC or GA, respectively. Patients who underwent I-MRI-guided craniotomy with GA had the highest morbidity (8%) at hospital discharge. Conclusions: Our institutional experience suggests that AC under 3-Tesla I-MRI could be an option for glioma resection, although firm conclusions cannot be drawn given the limited and heterogenous nature of our data. Future multicenter trials comparing anesthetic and imaging modalities for glioma resection are recommended.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".