Population-based outcomes after brain radiotherapy in patients with brain metastases from breast cancer in the Pre-Trastuzumab and Trastuzumab eras
Bibliographic record
Abstract
PURPOSE: To evaluate the survival of patients with human epidermal growth factor receptor 2 (HER2) positive and negative metastatic breast cancer irradiated for brain metastases before and after the availability of trastuzumab (T). MATERIALS AND METHODS: Women diagnosed with brain metastasis from breast cancer in two eras between 2000 and 2007 (T-era, n = 441) and 1986 to 1992 (PreT-era, n = 307), treated with whole brain radiotherapy (RT) were identified. In the T-era, HER2 testing was part of routine clinical practice, and in the preT-era 128/307 (42%) cases had HER2 testing performed retrospectively on tissue microarrays. Overall survival (OS) was estimated using the Kaplan-Meier method and comparisons between eras used log-rank tests. RESULTS: In the preT- and T-era cohorts, the rate of HER2 positivity was 40% (176/441) and 26% (33/128) (p < 0.001). The median time from diagnosis to brain RT was longer in the preT-era (3.3 years versus 2.3 years, p < 0.001). Survival after brain RT was improved in the T-era compared to the preT-era (1-year OS 26% versus 12%, p < 0.001). The 1-year OS rate for HER2 negative patients was 20% in both eras (p = 0.97). Among HER2 positive patients, the 1-year OS in the preT-era was 5% compared to 40% in the T-era (p < 0.001). CONCLUSIONS: Distinct from patients with HER2 negative disease in whom no difference in survival after brain RT was observed over time, patients with HER2 positive brain metastases experienced significantly improved survival subsequent to the availability of trastuzumab.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 | 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".