Triple-negative breast cancer: A population-based description of clinical-pathologic correlates and survival outcomes as a function of age at diagnosis.
Bibliographic record
Abstract
e12530 Background: Triple Negative Breast Cancer (TNBC) accounts for approximately 10% of invasive breast malignancies and is characterized by a relative lack of adjuvant systemic therapeutic options. Our aim was to describe a population-based cohort of primary TNBC patients including detection method, breast density at diagnosis, pathologic characteristics, and clinical outcomes, as a function of age at diagnosis. Methods: All cases of primary TNBC in Nova Scotia, Canada were identified through the Information System of the Nova Scotia Breast Cancer Screening Program (NSBSP). The study population included all female patients who underwent an open surgical biopsy following an imaging procedure, between January1 2005-13, and for whom pathological confirmation of TNBC was documented. A descriptive analysis of subjects’ clinical profile was performed, stratified by age group at diagnosis (≤49, 50-59, 60-69, and ≥70). Survival analysis techniques were used to model both disease-free (DFS) and overall survival (OS) as a function of age group, controlling for clinical-pathologic features. Results: A total of 421 cases were identified with a median follow-up time of 3.7 years. For the entire cohort, median tumor size was 2.3cm (range 0.1-14cm), and 175 (41.6%) had lymph node involvement. 348 (82.7%) had grade 3 disease with a significant difference in the distribution of grade across age groups (Chi-squared: p=0.01). Twenty-two (19.8%) cases in the 50-59 age group were interval cancers with a trend to shorter median OS in this age group compared to older cohorts (p= 0.07). There have been 100 deaths thus far with breast cancer specific survival rates remaining to be ascertained. Conclusions: Our observations suggest heterogeneity in a number of clinical-pathologic characteristics and survival outcomes within a large population-based cohort of TNBC.
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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 | 0.001 | 0.002 |
| 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".