NCOG-11. POST-RADIOSURGICAL OUTCOMES OF CYSTIC VESTIBULAR SCHWANNOMAS, A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract INTRODUCTION Vestibular schwannomas (VS) are benign neoplasms that present as cystic or solid variants. Cystic VS are associated with fast and unpredictable growth patterns, and their adherence to nearby structures can lead to poor surgical outcomes. While radiosurgery (RS) has gained popularity for VS, the heterogeneity of literature necessitates a systematic review. METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), PubMed, EMBASE, Web of Science, and Cochrane were searched for observational studies reporting cystic and solid VS radiosurgical outcomes. Pooled estimates were calculated using random-effect models with generic inverse variance to compare tumor control rates between cystic and solid VS. Quality assessment was done using the Newcastle Ottawa Criteria (NOS). RESULTS The search yielded 2,989 studies from which 680 were selected for full-text screening and 6 were included in this review and meta-analysis. The quality of studies ranged between good (3 studies, NOS 7/9), fair (2 studies, NOS 6/9), and poor (1 study, NOS 4/9). The patient pool included 1,070 solid and 347 cystic VS, all treated by gamma knife RS. No difference in tumor control was observed (RR: 1.02, 95%CI 0.91-1.16, p=0.7). 3 studies reported post-RS complications. Haseguawa reported hydrocephalus in 7 of 74 cystic cases. Ryu reported 1 case of hydrocephalus and 1 case of trigeminal pain in their 14 cystic cases. Shirato reported 5 transient trigeminal Neuralgia, 1 transient vertigo and 1 shunt operation in their 20 cystic cases. CONCLUSION Evidence presented in this meta-analysis supports the safety and efficacy of radiosurgery in treating cystic VS.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.034 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".