Navigated Intraoperative 2-Dimensional Ultrasound in High-Grade Glioma Surgery: Impact on Extent of Resection and Patient Outcome
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Bibliographic record
Abstract
BACKGROUND: Maximizing extent of resection (EOR) and reducing residual tumor volume (RTV) while preserving neurological functions is the main goal in the surgical treatment of gliomas. Navigated intraoperative ultrasound (N-ioUS) combining the advantages of ultrasound and conventional neuronavigation (NN) allows for overcoming the limitations of the latter. OBJECTIVE: To evaluate the impact of real-time NN combining ioUS and preoperative magnetic resonance imaging (MRI) on maximizing EOR in glioma surgery compared to standard NN. METHODS: We retrospectively reviewed a series of 60 cases operated on for supratentorial gliomas: 31 operated under the guidance of N-ioUS and 29 resected with standard NN. Age, location of the tumor, pre- and postoperative Karnofsky Performance Status (KPS), EOR, RTV, and, if any, postoperative complications were evaluated. RESULTS: The rate of gross total resection (GTR) in NN group was 44.8% vs 61.2% in N-ioUS group. The rate of RTV > 1 cm3 for glioblastomas was significantly lower for the N-ioUS group (P < .01). In 13/31 (42%), RTV was detected at the end of surgery with N-ioUS. In 8 of 13 cases, (25.8% of the cohort) surgeons continued with the operation until complete resection. Specificity was greater in N-ioUS (42% vs 31%) and negative predictive value (73% vs 54%). At discharge, the difference between pre- and postoperative KPS was significantly higher for the N-ioUS (P < .01). CONCLUSION: The use of an N-ioUS-based real-time has been beneficial for resection in noneloquent high-grade glioma in terms of both EOR and neurological outcome, compared to standard NN. N-ioUS has proven usefulness in detecting RTV > 1 cm3.
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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 | 0.001 |
| 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 it