GP.05 The risk of malignancy after stereotactic radiosurgery
Bibliographic record
Abstract
Background: A major concern of patients undergoing Gamma Knife radiosurgery (GKS) for benign tumors and other conditions is the risk of a separate secondary malignancy or malignant -transformation. The incidence of radiosurgery-associated malignancy based on long-term follow-up remains unknown. Methods: We conducted a population-based cohort study to estimate the incidence rate of both malignant transformation and a separate radiation-associated malignancy in patients undergoing GKS from 1987 to 2016 at 5 centers. Results: 11 527 patients underwent radiosurgery for meningioma (n=3261), arteriovenous malformation (n=2868), trigeminal neuralgia (n=1982), vestibular schwannoma (n=1957), pituitary adenoma (n=1193), other (n=266). The follow-up time ranged from 0.3 to 23.8 years. Four cases of malignant transformation and 3 new malignant brain tumors were reported, two of which were not within the irradiated field. The incidence of malignant transformation was 6.6 per 100 000 patient-years and of new malignancy, either locally or distant, was 5 in 100 000 patient-years. These risks are not higher than the Central Brain Tumor Registry of the United States derived annual incidence rate of all primary malignant CNS tumors of 7.15 per 100 000. Conclusions: Physicians can safely counsel patients that the risk of malignancy after stereotactic radiosurgery remains extremely low, even at long-term follow-up of greater than 10 years.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.092 | 0.018 |
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".