Minimally Invasive Approach for the Resection of Spinal Neoplasm
Bibliographic record
Abstract
STUDY DESIGN: Retrospective Case Series. OBJECTIVE: To determine if extradural, intradural extramedullary, and intramedullary spinal neoplasms can be safely resected through a minimally invasive corridor. SUMMARY OF BACKGROUND DATA: The use of minimally invasive approaches for resection of spinal neoplasms has been described for intradural schwannomas and ependymomas. We demonstrate that this approach can be extended to the resection of a variety of extradural, intradural and intramedullary spinal tumors. METHODS: We undertook a retrospective review of all patients presenting with clinical and radiographic evidence of spinal neoplasm that subsequently underwent a minimally invasive approach for resection of the tumor using the METRx MAST QUADRANT Retractor System (Medtronics, Memphis, TN). Primary endpoints analyzed include completeness of resection, postoperative neurologic status, operative time, blood loss, postoperative pain, length of hospital stay, and operative complications. RESULTS: Two cervical, seven thoracic and 13 lumbar neoplasms were identified in 20 patients operated on between September 2005 and May 2009. Mean intraoperative time was 210 minutes, blood loss 428 mL and average length of hospital stay was 3 days. Four patients required postoperative patient-controlled analgesia for pain control and an average of 5.8 doses of narcotic were given per patient. Two patients developed postoperative complications. Fifteen of 22 tumors (68%) were completely resected, with only one patient requiring repeat operation for residual tumor. All but one patient were improved from preoperative status at 6 months. CONCLUSION: Intramedullary, intradural and extradural spinal neoplasms can be resected through a minimally invasive approach without increased risk for adverse neurologic outcome. This technique may be an appropriate alternative to the open approach for well-circumscribed extramedullary lesions spanning one or two spinal levels. With increasing experience, reduced operative time, blood loss, complications, length of hospital stay, postoperative pain, and spinal instability may be seen.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".