Minimally invasive removal of a giant extradural lumbar foraminal schwannoma
Bibliographic record
Abstract
BACKGROUND: Purely extradural lumbar schwannomas are rare lesions. Resection traditionally requires an open laminectomy and ipsilateral complete facectomy. Recent reports have demonstrated safety and efficacy of removal of these tumors using mini-open access devices with expandable retractors. We report a case of a giant L3 schwannoma successfully resected through a minimally invasive approach using the non-expandable Spotlight tubular retrator (Depuy Spine). CASE DESCRIPTION: A 77-year-old woman presented with a history of chronic right leg pain, paresthesias and proximal right leg weakness. Magnetic Resonance imaging (MRI) scan revealed a large dumbbell-shaped extradural foraminal lesion at the L3-L4 level with significant extraforaminal extension. The patient underwent a minimally invasive gross total resection (GTR) of the tumor using an 18-mm Spotlight tubular retractor system. Pathology confirmed the lesion to be a benign schwannoma. Postoperatively, the patient's symptoms resolved and she was discharged from the hospital on the second postoperative day. Postoperative MRI showed no residual tumor. The patient returned to normal activities after 2 weeks and remained asymptomatic with no neurological deficits at final 6 months follow-up. CONCLUSION: Giant lumbar extradural schwannomas can be safely and completely resected using minimally invasive surgery without the need for facectomy or subsequent spinal fusion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".