P.093 Thorascopic assisted resection of dumbbell nerve sheath tumors
Bibliographic record
Abstract
Background: Surgery to remove dumbbell nerve sheath tumors (NST) is complex, and is accompanied by significant operative and perioperative challenges. Historically, resection of dumbbell NST required large operations involving opening the chest and laminectomy, often accompanied by instrumentation. We describe a case series of 5 patients who underwent single stage thorascopic-guided resection of dumbbell schwannoma at our institution. Methods: 5 cases presented consisted of moderate to large NST, which contained intraforaminal components. Tumor location ranged from T3-T9, with most tumors spanning 2-3 vertebral bodies. Presentation ranged from discomfort/pain (most common) to one presentation of neurologic deficit with difficulty with ambulation. Results: Thorascopic assisted resection accomplished gross total resection in 4 of the 5 cases. In all cases there was no significant neurologic deficit, although one patient reported transient numbness following the operation and all patients made significant improvement post operatively. The length of stay for these cases ranged from 1-6 days. Conclusions: Thorascopic assisted resection of dumbbell NST can be performed safely and with good outcomes by using the corridor the tumor produces. This approach reduces the need for instrumentation, length of stay and post operative complication rates relative to traditional approaches. To perform this approach effectively, good co-operation between the neurosurgeon and thoracic surgeon needs to be present.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".