Early Stage Triple Negative Breast Cancer Has Significantly Better Outcomes than More Advanced Disease: A Single Centre Retrospective Review
Bibliographic record
Abstract
A retrospective, serial analysis of 181 triple negative breast cancer (TNBC) patients was undertaken at a regional cancer centre in Canada. The primary focus of the analysis was to investigate the effect of presenting stage in patients with TNBC on progression free and overall survival. We were able to demonstrate that patients presenting with an earlier stage breast cancer had a significantly superior progression free and overall survival when compared to more advanced stage. The adjusted multivariate cox-regression analyses for the overall and progression free survival suggest that the hazard of death was significantly lower for patients with stages I (HR = 0.09; 95% CI 0.03 - 0.24) and II (HR = 0.29; 95% CI 0.16 - 0.54) than for patients with stage III. The only other predictor of progression free survival besides stage, was receipt of radiotherapy (HR = 0.39; 95% CI 0.22 - 0.69) in the adjusted cox regression analysis. Less than 2% of patients presented with stage IV disease. The small numbers presenting with stage IV disease may have impact on the development of clinical and translational trials. Certainly there may be stage migration if staging included more standardized or more sensitive investigations such as PET scans, and this might an important consideration in developing clinical trials. Twenty-five percent of patients presented with stage I disease. It is important for patients with TNBC presenting with earlier stages of disease that they are aware that they will have a better prognosis than their counterparts with more advanced disease. It is important that we are aware of this patient population, as their treatment recommendations are unclear and a source of a fair amount of controversy currently.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".