Does age affect outcome with breast cancer?
Bibliographic record
Abstract
Prior data about the influence of age at diagnosis of breast cancer on patient outcomes and survival has been conflicting. Using the Breast Cancer Outcomes Unit database at BC Cancer, this retrospective population-based study identified a cohort of 24,469 patients diagnosed with invasive breast cancer between 2005 and 2014. Median follow-up was 11.5 years. We analyzed clinical and pathological features at diagnosis and treatment specific variables compared across the following age cohorts: <35, 35-39, 40-49, 50-59, 60-69, 70-79, and 80 years of age and older. We assessed the impact of age on breast cancer specific survival (BCSS) and overall survival (OS) by age and subtype. There were distinct clinical-pathological and treatment pattern differences at both extremes of age at diagnosis. Patients <35 and 35-39 years old were more likely to present with higher risk features, HER2 positive or triple-negative biomarkers, and more advanced TNM stage at diagnosis. They were more likely to undergo treatment with mastectomy, axillary lymph node dissection, radiotherapy and chemotherapy. Conversely, patients ≥80 years old were generally more likely to have hormone-sensitive HER2-negative disease, and lower TNM stage at diagnosis. They were less likely to undergo surgery or be treated with radiotherapy and chemotherapy. Both younger and elderly age at breast cancer diagnosis were independent risk factors for poorer prognosis after controlling for subtype, LVI, stage, and treatment factors. This work will help clinicians to more accurately estimate patient outcomes, patterns of relapse, and provide evidence-based treatment recommendations.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.001 |
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".