Indications, feasibility, safety, and efficacy of CyberKnife radiotherapy for the treatment of olfactory groove meningiomas: a single institutional retrospective series
Bibliographic record
Abstract
Abstract Purpose To assess the safety and efficacy of CyberKnife® radiotherapy (CKRT) for the treatment of olfactory groove meningiomas (OGMs). Methods A retrospective review was performed of 13 patients with OGM treated with CKRT from September 2005 to May 2018 at our institution. Nine patients were treated primarily with CKRT, 3 for residual disease following resection, and 1 for disease recurrence. Results Five patients were treated with stereotactic radiosurgery (SRS), 6 with hypofractionated stereotactic radiotherapy (HSRT), and 2 with fractionated stereotactic radiotherapy (FSRT). The median tumor volume was 8.12 cm3. The median prescribed dose was 14.8 Gy for SRS, 27.3 Gy for HSRT, and 50.2 Gy for FSRT. The median maximal dose delivered was 32.27 Gy. Median post treatment follow-up was 48 months. Twelve of 13 patients yielded a 100% regional control rate with a median tumor volume reduction of 31.7%. Six of the 12 patients had reduced tumor volumes while the other 6 had no changes. The thirteenth patient had significant radiation-induced edema requiring surgical decompression. Twelve patients were alive and neurologically stable at the time of the review. One patient died from pneumonia unrelated to his CKRT treatment. Conclusions CKRT appears to be safe and effective for the treatment of OGMs.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".