TUMOR BED RADIOSURGERY AFTER RESECTION OF CEREBRAL METASTASES
Bibliographic record
Abstract
OBJECTIVE: Adjuvant irradiation after resection of brain metastases reduces the risk of local recurrence. Whole-brain radiation therapy can be associated with significant neurotoxicity in long-term survivors of brain metastases. This retrospective study evaluates the role of tumor bed stereotactic radiosurgery as an alternative method of irradiation after initial resection of brain metastases to prevent local recurrence. METHODS: Forty patients underwent tumor bed radiosurgery after resection of brain metastases at two separate academic medical centers. The median age was 59.5 years. Twenty patients (67.5%) had single metastases. Resection was complete in 80% and partial in 20% of the patients. At the time of radiosurgery, systemic disease was active in 57.5%, inactive in 32.5%, and in remission in 10% of the patients. The median Karnofsky Performance Scale score was 80% (range, 60-100%). Radiosurgery was performed a median of 4 weeks after tumor resection. The median cavity radiosurgery volume was 9.1 ml (range, 0.6-39.9 ml). The median margin and maximum radiation dose were 16 and 32 Gy, respectively. RESULTS: Local control at the resection site was achieved in 73% of patients at a median follow-up period of 13 months. No variable significantly affected local control. New remote brain metastases occurred in 54% of the patients. Symptomatic radiation effect was seen in 5.4% of the patients. The median survival was 13 months after radiosurgery (range, 2-56 mo). CONCLUSION: Tumor bed radiosurgery provides effective local control of the tumor after resection in most patients. These preliminary data support radiosurgery after resection rather than traditional radiation therapy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".