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 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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".