Risk analysis of radiosurgery for vestibular schwannoma: Systematic review and comparative study of 10-year outcomes
Bibliographic record
Abstract
Background: Determine the benefit of stereotactic radiosurgery (SRS) compared to no treatment for sporadic vestibular schwannoma (VS) by calculating epidemiologic risk using 10-year data; apply the analysis to VS that have demonstrated linear growth. Methods: PubMed, Google Scholar, Web of Science, and Cochrane Library are systematically reviewed for VS tumor control 10 years after SRS and compared to a historical cohort of untreated VS (primary risk analysis). Subgroups of VS limited by size and observed growth are compared to the untreated cohort (secondary analysis). Results: Twenty-four studies of 4079 SRS-treated VS exhibited tumor control in 90.93% (87.0%-100%; SD 4.1%), while 1959 untreated VS exhibited control in 65.24%. SRS reduces the absolute risk (ARR) of tumor progression by 25.7% compared to no treatment. The number needed to treat (NNT) is 4 (3.892, 95% CI: 3.619-4.210). Subgroup analyses of (1) VS with definite linear growth before SRS result in a similar ARR of 29.4% and NNT 4 (3.395, 95% CI: 2.966-3.968), and (2) Koos 1 VS result in lower ARR 18.31% and higher NNT 6 (5.209; 95% CI: 4.018-7.401). Conclusions: This "best-available" case-control study of 10-year data reveals that ARR and NNT are similar for VS with and without definite pretreatment linear growth. These comparisons may be applied to CPA diameters less than 2 cm. Results for Koos 1 tumors are different. This analysis quantifies the therapeutic benefit of SRS by comparative risk analysis. The level of evidence on this topic is low.
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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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.018 |
| Bibliometrics | 0.010 | 0.008 |
| 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) | 0.003 | 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".