Triple-Negative Breast Cancer: Clinical Features and Patterns of Recurrence
Bibliographic record
Abstract
PURPOSE: To compare the clinical features, natural history, and outcomes for women with "triple-negative" breast cancer with women with other types of breast cancer. EXPERIMENTAL DESIGN: We studied a cohort of 1,601 patients with breast cancer, diagnosed between January 1987 and December 1997 at Women's College Hospital in Toronto. Triple-negative breast cancers were defined as those that were estrogen receptor negative, progesterone receptor negative, and HER2neu negative. The prognostic significance of triple-negative breast cancer was explored. RESULTS: The median follow-up time of the 1,601 women was 8.1 years. One hundred and eighty of 1,601 patients (11.2%) had triple-negative breast cancer. Compared with other women with breast cancer, those with triple-negative breast cancer had an increased likelihood of distant recurrence (hazard ratio, 2.6; 95% confidence interval, 2.0-3.5; P < 0.0001) and death (hazard ratio, 3.2; 95% confidence interval, 2.3-4.5; P < 0.001) within 5 years of diagnosis but not thereafter. The pattern of recurrence was also qualitatively different; among the triple-negative group, the risk of distant recurrence peaked at approximately 3 years and declined rapidly thereafter. Among the "other" group, the recurrence risk seemed to be constant over the period of follow-up. CONCLUSIONS: Triple-negative breast cancers have a more aggressive clinical course than other forms of breast cancer, but the adverse effect is transient.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".