A generalizable relationship between mortality and time-to-death among breast cancer patients can be explained by tumour dormancy
Bibliographic record
Abstract
BACKGROUND: Women with ER-positive breast cancer may recur as late as 20 years post-diagnosis. The reason for this delayed recurrence is unknown. We studied survival patterns, including time-to-death in 123,705 women with stage I to III invasive breast cancer, enrolled in the SEER database. Among these 76.8% were ER-positive and 23.2% were ER-negative. METHODS: We divided the cohort into ten classes with varying risks of death from breast cancer. The 20-year mortality for women in the highest risk decile 10 was 69% versus 5% for women in the lowest decile 1. The difference in the time-to-death by decile could be explained by a variable α which represents the annual rate of reactivation from tumour dormancy. RESULTS: The duration of tumour dormancy was much longer, on average, for ER-positive breast cancers than for ER-negative breast cancers. Reactivation from tumour dormancy appears to occur at random and may explain the very long time to cancer recurrence in women with small node-negative ER-positive breast cancers. CONCLUSION: The clinical course of women with low-risk ER-positive breast cancer is inherently unpredictable and consequently death is equally as likely to occur at year 3 than at year 20.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".