Population-based outcomes after whole brain radiotherapy and re-irradiation in patients with metastatic breast cancer in the trastuzumab era
Bibliographic record
Abstract
PURPOSE: This study examined the population-based use and outcomes of brain radiotherapy (BRT) for brain metastases (BM) from breast cancer with a focus on repeat BRT in the trastuzumab era. METHODS AND MATERIALS: All women with breast cancer diagnosed from 2000-2007 and treated with BRT were retrospectively identified from a provincial database. RESULTS: A total of 441 women with BM from breast cancer were identified. The median age was 55 years and 40% (176/441) had human epidermal growth factor receptor 2 (HER2) positive disease. The median survival (MS) from the initial BRT for all 441 women was 4.5 months. The MS by Radiation Therapy Oncology Group Recursive Partitioning Analysis (RPA) class was: 1 (14.5 months), 2 (6.4 months) and 3 (1.8 months). For the 37 cases receiving repeat BRT, 27% (10/37) had stereotactic radiosurgery (SRS) and 70% (26/37) had HER2 positive disease, of which, 81% (21/26) received trastuzumab in the metastatic setting. For repeat BRT, the median survival by RPA class was: 1 (9.8 months), 2 (7.4 months) and 3 (2.0 months). For RPA class 1 and 2, the one-year overall survival (OS) was 45%. CONCLUSION: The proportion of cases with HER2 positive disease was increased at repeat BRT compared to initial BRT. RPA class 1 and 2 patients should be considered for repeat BRT.
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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 | 0.002 |
| 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 | 0.000 |
| 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".