Wide Dissection Trans-Sulcal Approach for Resection of Deep Intra-Axial Lesions in Eloquent Brain Areas
Bibliographic record
Abstract
Introduction: Resection of intra-axial tumors (IaT) in eloquent brain regions risks major postoperative neurological deficits. Awake craniotomy is often used to navigate these areas; however, some patients are ineligible for awake procedures. The trans-sulcal approach (TScal) was introduced to reduce parenchymal trauma during tumor resection. We report our experiences utilizing TScal for resection of deep IaT located in eloquent areas. Materials and Methods: This is a single-center retrospective analysis of patients who underwent IaT resection in eloquent areas via TScal from January 2013 to April 2021. Seventeen cases were reviewed, and relevant data was collected. Fluorescence-guided surgery with 5-aminolevulinic acid (ALA) and intraoperative ultrasound was performed in some cases. Results: Seventeen patients (10 males, 7 females) averaging 61.2 years-old (range, 21–76) were included in this study. Average length of stay was 4.8 days, and only 2 patients (11.8%) required hospital readmission within 30 days. Gross total resection (GTR) was achieved in 15 patients (88.2%), while subtotal resection occurred in 2 patients (11.8%). Eleven patients (64.7%) reported full resolution of symptoms, 4 patients (23.5%) reported deficit improvement, and 2 patients (11.8%) experienced no change from their preoperative deficits. No patient developed new permanent deficits postoperatively. Discussion: GTR, preoperative deficit reduction, and complications were comparable to awake craniotomy and other TScal studies. Ancillary intraoperative techniques, such as brain mapping, 5-ALA and intraoperative ultrasound, are afforded by TScal to improve resection rates and overall outcomes. Conclusions: TScal can be an option for patients with deep lesions in eloquent areas who are not candidates for awake surgeries.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".