Clinical Characteristics, Management, and Outcomes of Intramedullary Spinal Cord Ependymomas: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Intramedullary spinal cord tumors (IMSCTs) are rare tumors, with ependymoma being the most common type. Surgical resection is the main treatment, but gross total resection (GTR) carries a risk of morbidity, while subtotal resection (STR) increases the risk of recurrence. The role of adjuvant radiotherapy is debated, and chemotherapy is rarely used except for recurrence. This study aims to investigate and compare the clinical characteristics, management, and outcomes of intramedullary spinal cord ependymomas (ISCEs) comprehensively. Methods: This systematic review and meta-analysis followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzing clinical characteristics, management, and outcomes of ISCEs. Studies were screened using Population, Intervention, Comparison, Outcome, and Study design criteria, quality-assessed with Newcastle-Ottawa Scale and Risk Of Bias In Non-randomized Studies Of Interventions, and statistically analyzed with Review Manager 5.4.1 and R software, evaluating treatment efficacy and prognostic factors. Results: GTR improves neurological function, reduces recurrence, and improves survival in IMSCTs, while STR increases the risk of recurrence, which often requires radiotherapy. The meta-analysis indicates that patients who received optimal management strategies had significantly better neurological outcomes (odds ratio: 4.65; 95% confidence interval: 1.77-12.23; p=0.0018). Meta-analysis also showed that based on the management approach, the rate of complication, improvement, recurrence, and overall survival was 16%, 71%, 7%, and 18%, respectively. Conclusions: Surgical strategies, individualized treatment, and advanced monitoring optimize IMSCT outcomes. Future research should standardize protocols, conduct large-scale studies, and refine adjuvant therapies to improve prognosis and quality of life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".