Cancer-Specific Outcomes in the Elderly with Triple-Negative Breast Cancer: A Systematic Review
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) is more common among young women, although it frequently presents in older patients. Despite an aging population, there remains a paucity of data on the treatment of TNBC in elderly women. We conducted a systematic review of the peer-reviewed and unpublished literature that captures the management and breast-cancer-specific survival (BCSS) of women ≥70 years old with TNBC. Out of 739 papers, five studies met our inclusion criteria. In total, 2037 patients with TNBC treated between 1973 and 2014 were captured in the analysis. Women ≥70 years old were less likely to undergo surgical resection compared to those <70 (92.8% vs. 94.6%, p = 0.002). Adjuvant therapy, including radiation and chemotherapy, was also less likely to be utilized in women ≥70 years of age. These treatment differences were associated with more than a doubling of cancer-specific mortality in the elderly cohort (5.9% vs. 2.7% in ≤70 years old, p < 0.0001). Two of the five studies showed improved BCSS with adjuvant treatment while others showed no difference. Our systemic review questions the appropriateness of therapeutic de-escalation in this cohort and highlights the significant gap in our understanding of the optimal management for elderly patients with TNBC. Until more data are available, multidisciplinary treatment decision-making should carefully balance the available clinical evidence as well as the patient’s predicted life expectancy and goals-of-care preferences.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".