P.135 Dura splitting technique for surgical resection of spinal meningioma
Bibliographic record
Abstract
Background: Spinal meningiomas are intradural extramedullary tumors that account for 25–46% of all primary spinal tumors. A growing body of literature suggests that the extent of resection significantly affects the recurrence rate of spinal meningiomas and that Simpson grade II resection may not be as adequate as previously thought. Dura Splitting Technique (DST) can be used with no major perioperative complications. Methods: Retrospect review of six cases of spinal meningiomas where DST was used. The patients ranged in age at presentation from 38 to 80 years. All presented with symptoms including gait unsteadiness and lower limbs weakness. Spinal MRI was used to establish the diagnosis. All of the tumors were located ventral or ventrolateral to the spinal cord. Results: DST was applying to spinal meningioma cases,complete tumor resection by separating the involved dura into inner and outer layer. Preserving the dura outer layer and avoiding the need for dural graft reconstruction and CSF leak. A total of six cases, four in thoracic spine and two in cercical spine one anterior and one posterior, all four cases had no reported surgical complications or tumor recurrenc. Conclusions: We confirm that DST is safe and a superior method in the treatment of spinal meningiomas.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".