Brain Tumor Surgery with the Toronto Open Magnetic Resonance Imaging System: Preliminary Results for 36 Patients and Analysis of Advantages, Disadvantages, and Future Prospects
Bibliographic record
Abstract
OBJECTIVE: Frameless navigation systems represent a huge step forward in the surgical treatment of intracranial pathological conditions but lack the ability to provide real-time imaging feedback for assessment of postoperative results, such as catheter positions and the extent of tumor resections. An open magnetic resonance imaging system for intracranial surgery was developed in Toronto, by a multidisciplinary team, to provide real-time intraoperative imaging. METHODS: The preliminary experience with a 0.2-T, vertical-gap, magnetic resonance imaging system for intraoperative imaging, which was developed at the University of Toronto for the surgical treatment of patients with intracranial lesions, is described. The system is known as the image-guided minimally invasive therapy unit. RESULTS: Between February 1998 and March 1999, 36 procedures were performed, including 21 tumor resections, 12 biopsies, 1 transsphenoidal endoscopic resection, and 2 catheter placements for Ommaya reservoirs. Three complications were observed. All biopsies were successful, and the surgical goals were achieved for all resections. Problems included restricted access resulting from the confines of the magnet and the imaging coil design, difficulties in working in an operating room that is less spacious and familiar, inconsistent image quality, and a lack of nonmagnetic tools that are as effective as standard neurosurgical tools. Advantages included real-time imaging to facilitate surgical planning, to confirm entry into lesions, and to assess the extent of resection and intraoperative and immediate postoperative imaging to confirm the extent of resections, catheter placement, and the absence of postoperative complications. CONCLUSION: Intraoperative magnetic resonance imaging has great potential as an aid for intracranial surgery, but a number of logistic problems require resolution.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".