Brain Metastasis from Breast Cancer: risk factors and radiotherapy perspective from a tertiary Middle Eastern facility.
Bibliographic record
Abstract
BACKGROUNDS: Breast cancer represents the second most frequent cause of brain metastases after lung cancer. Previous studies have identified the subgroups of patients with triple-negative and HER2-positive as having an increased risk for the development of brain metastases. We are not aware in Kurdistan - Iraq of any national studies that are in parallel with these findings. PATIENTS AND METHODS: A cross-sectional descriptive study conducted on 57 patients who were known cases of breast cancer with brain metastasis, managed with whole brain radiotherapy at a tertiary radiotherapy institute in two years (January 2015 to December 2016), as a convenient sample. Data were collected from patients' archives and phone calls and then analyzed using SPSS version 23. RESULTS: Younger age at diagnosis and cancers with HER2-positive receptor phenotype are risk factors for brain metastasis. Median survival post-brain metastasis is significantly affected by receptor phenotypes (2 months in triple negative versus 7 months in hormone receptor positive) and performance status (18 months if performance score of 70% and above versus 1.5 months if it is 60% and less). CONCLUSION: Primary breast cancer patients have more risk to develop brain metastases if they are at younger age and HER2-positive and the survival post-brain metastases is dramatically affected by both triple negative receptor phenotype and lower performance score.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".