Single-fraction radiosurgery outcomes for large vestibular schwannomas in the upfront or post-surgical setting: a systematic review and International Stereotactic Radiosurgery Society (ISRS) Practice Guidelines
Bibliographic record
Abstract
PURPOSE: To perform a systematic review of literature specific to single-fraction stereotactic radiosurgery (SRS) for large vestibular schwannomas (VS), maximum diameter ≥ 2.5 cm and/or classified as Koos Grade IV, and to present consensus recommendations on behalf of the International Stereotactic Radiosurgery Society (ISRS). METHODS: The Medline and Embase databases were used to apply the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. We considered eligible prospective and retrospective studies, written in the English language, reporting treatment outcomes for large VS; SRS for large post-operative tumors were analyzed in aggregate and separately. RESULTS: 19 of the 229 studies initially identified met the final inclusion criteria. Overall crude rate of tumor control was 89% (93.7% with no prior surgery vs 87.7% with prior surgery). Rates of salvage microsurgical resection, need for shunt, and additional SRS in all series versus those with no prior surgery were 9.6% vs 3.3%, 4.7% vs 6.4% and 1% vs 0.9%, respectively. Rates of facial palsy and hearing preservation in all series versus those with no prior surgery were 1.3% vs 3.4% and 34.2% vs 40.4%, respectively. CONCLUSIONS: Upfront SRS resulted in high rates of tumor control with acceptable rates of facial palsy and hearing preservation as compared to the results in those series including patients with prior surgery (level C evidence). Therefore, although large VS are considered classic indication for microsurgical resection, upfront SRS can be considered in selected patients and we recommend a prescribed marginal dose from 11 to 13 Gy (level C evidence).
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".