Tumour Size Predicts Long-Term Survival among Women with Lymph Node-Positive Breast Cancer
Bibliographic record
Abstract
BACKGROUND: The benefit of early detection of breast cancer is assumed to be achieved primarily by identifying disease before it has spread beyond the breast. In support of early detection, the survival experience of women with breast cancer decreases as the mean size of the cancer increases. It is not clear if women with regional spread (node-positive breast cancer) benefit from early detection to the same extent that women with node-negative breast cancer do. METHODS: A review was conducted of the survival experience of 1894 patients with invasive breast cancers 5.0 cm or less in size. Cases were divided into node-positive and node-negative, and tumours were categorized by size (0.1-1.0 cm, 1.1-2.0 cm, and 2.1-5.0 cm). After a mean follow-up of 9.9 years, 368 cancer-specific deaths had occurred in the cohort. The effect of tumour size on 15-year survival for subgroups of women with node-positive and node-negative breast cancer was estimated. RESULTS: Tumour size was a strong predictor of 15-year survival in both the node-positive and node-negative cancer subgroups. A decline of 1.0 cm in size was associated with a reduction in 15-year mortality of 10.3% in the node-positive group and of 2.5% in the node-negative group. A decline of approximately 1.5 cm was associated with a reduction in mortality of 23.0% in the node-positive group and of 10.8% in the node-negative group. CONCLUSIONS: The impact of decreasing tumour size on 15-year survival is much greater for women with node-positive than for women with node-negative breast cancers. Contrary to expectation, the benefit of screening is likely to be greater for women with relatively advanced breast cancer than for women with earlystage disease.
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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 | 0.004 |
| 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".