Gamma Knife Radiosurgery for Large Vestibular Schwannomas: A Canadian Experience
Bibliographic record
Abstract
OBJECT: To review our institutional experience with Gamma Knife (GK) stereotactic radiosurgery in treating large vestibular schwannomas (VS) of 3 to 4 cm diameter. METHODS: We conducted a retrospective cohort review of all patients treated with GK for VS at our institution between November 2003 and March 2012. Data on age, sex, VS volume, location and maximal diameter, House-Brackmann (HB) facial nerve scores pre and post-GK, Gardner-Robertson (GR) hearing score pre and post-GK, GK treatment parameters, VS response time, complications and clinical outcome was recorded RESULTS: A total of 28 patients during the defined time period were identified. Three patients were lost to follow-up. Mean follow-up was 34.5 months. Tumor control occurred in 92%, and was maintained in 85.7% at two years. Facial nerve or hearing preservation occurred in all treated compared to pre-GK status, as per HB and GR grading. Transient complications occurred in 80%. Temporary vestibular dysfunction occurred in seven patients (28%). One patient (4%) had the permanent complication of worsening pre-GK hemifacial spasm. Four patients (16%) developed hydrocephalus post-GK. CONCLUSION: GK stereotactic radiosurgery as a primary treatment modality for large VS can provide acceptable tumor control rates with good facial nerve and hearing preservation, and low complication rates.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".