Minimally Invasive Surgery for Cervical Meningioma: A Systematic Review and Case Series
Bibliographic record
Abstract
BACKGROUND: Meningiomas are benign spinal arachnoid tumours, typically presenting as intradural extramedullary (IDEM) lesions that can compress the spinal cord and require surgical intervention. Minimally invasive surgery (MIS) techniques like mini-open, tubular and endoscopic approaches minimize tissue manipulation, reduce pain and accelerate recovery. This systematic review provides insights into current practices regarding MIS for cervical meningioma and presents a case series of eight patients with cervical meningioma effectively managed by MIS. METHODS: A comprehensive literature search was conducted across Embase, PubMed and Medline Ovid, focusing on MESH terms related to cervical vertebrae, nervous system neoplasms and minimally invasive surgical procedures. Risk of bias in retained studies was assessed using the Joanna Briggs Institute Critical Appraisal tools for case series and case reports. A narrative synthesis of our results is presented. RESULTS: Nine studies with 15 patients undergoing MIS for cervical meningioma were included. Most tumours were at the craniospinal junction. Gross total resection (Simpson grade 2) was achieved in 14 cases, with no reported post-operative complications. The length of stay (LOS) ranged from 2 to 6 days, and no tumour recurrence was observed. Our case series of eight patients confirmed MIS benefits, including shorter operative times, comparable surgical outcomes, and the avoidance of spinal deformities requiring instrumentation. CONCLUSION: In well-selected patients, MIS for cervical meningioma is a safe and effective procedure offering direct lateral access, minimal bony resection, limited soft tissue manipulation, and avoidance of cervical fusion, thereby minimizing post-operative complications and LOS.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".