Application of intraoperative high-field magnetic resonance imaging in pediatric neurosurgery
Bibliographic record
Abstract
OBJECT: Over the past decade, the use of intraoperative MR (iMR) imaging in the pediatric neurosurgical population has become increasingly accepted as an innovative and important neurosurgical tool. The authors summarize their experience using a mobile 1.5-T iMR imaging unit with integrated neuronavigation with the goal of identifying procedures and/or pathologies in which the application of this technology changed the course of surgery or modified the operative strategy. METHODS: A database has been prospectively maintained for this patient population. The authors reviewed the hospital charts and imaging results for all patients in the database. This review revealed 105 neurosurgical procedures performed in 98 children (49 male and 49 female) between March 1998 and April 2008. Intradissection (ID) and/or quality assurance images were obtained at the discretion of the surgeon. RESULTS: The median age at surgery was 12 years (4 months-18 years). One hundred intracranial and 5 spinal procedures were performed; 22 of these procedures were performed for recurrent pathology. Surgical planning scans were obtained for 102 procedures, and neuronavigation was used in 93 patients. The greatest impact of iMR imaging was apparent in the 55 procedures to resect neoplastic lesions; ID scans were obtained in 49 of these procedures. Further surgery was performed in 49% of the procedures during which ID scans had been obtained. A smaller proportion of ID scans in the different cranial pathology groups (5 of 21 epilepsy cases, 4 of 9 vascular cases) resulted in further resections to meet the surgical goal of the surgeon. Two ID scans obtained during 5 procedures for the treatment of spinal disease did not lead to any change in surgery. Postoperative scans did not reveal any acute adverse events. There was 1 intraoperative adverse event in which a Greenberg retractor was inadvertently left on during ID scanning but was removed after the scout scans. CONCLUSIONS: The application of iMR imaging in the pediatric neurosurgical population allows, at minimum, the opportunity to perform less invasive surgical exposures. Its potential is greatest when its high-quality imaging ability is coupled with its superior neuronavigation capabilities, which permits tracking of the extent of resection of intracranial tumors and, to a lesser extent, other lesions during the surgical procedure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".