Gamma Knife Radiosurgery in Recurrent Glioblastoma
Bibliographic record
Abstract
BACKGROUND: We evaluated Gamma Knife radiosurgery (GKRS) as a treatment option for patients with recurrent glioblastoma. PATIENTS AND METHODS: 42 patients with histopathologically diagnosed recurrent grade IV tumor were treated with GKRS. All patients had undergone standard multimodal first-line treatment. The average time from diagnosis to GKRS was 17.0 months. The median target volume was 5.1 cm3. The median margin dose was 10 Gy and the median central dose 20 Gy. In a subset of patients, O6-methylguanine methyltransferase (MGMT) promoter methylation analysis by pyrosequencing was performed. RESULTS: Most patients did not develop complications after GKRS. Time to radiological progression after initial GKRS was 4.4 months (95% CI: 3.1-5.7 months). Radiological progression mainly occurred beyond the GKRS-irradiated area. The median survival time after initial GKRS was 9.6 months (95% CI: 7.7-11.5 months). The median overall survival time from diagnosis was 25.6 months (95% CI: 21.8-29.3 months). Patients with MGMT promoter methylation survived significantly longer (33.4 months; 95% CI: 21.2-45.5 months) compared to patients without MGMT promoter methylation (16.0 months; 95% CI: 8.0-23.9 months). CONCLUSION: GKRS seems to be a relatively safe salvage treatment option for recurrent glioblastoma for highly selected patients but must be seen as part of a multimodal treatment algorithm.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".