Risk of second cancer among women with breast cancer
Bibliographic record
Abstract
A large number of women survive a diagnosis of breast cancer. Knowledge of their risk of developing a new primary cancer is important not only in relation to potential side effects of their cancer treatment, but also in relation to the possibility of shared etiology with other types of cancer. A cohort of 525,527 women with primary breast cancer was identified from 13 population-based cancer registries in Europe, Canada, Australia and Singapore, and followed for second primary cancers within the period 1943-2000. We used cancer incidence rates of first primary cancer for the calculation of standardized incidence ratios (SIRs) of second primary cancer. Risk of second primary breast cancer after various types of nonbreast cancer was also computed. For all second cancer sites combined, except contralateral breast cancer, we found a SIR of 1.25 (95% CI = 1.24-1.26) on the basis of 31,399 observed cases after first primary breast cancer. The overall risk increased with increasing time since breast cancer diagnosis and decreased by increasing age at breast cancer diagnosis. There were significant excesses of many different cancer sites; among these the excess was larger than 150 cases for stomach (SIR = 1.35), colorectal (SIR = 1.22), lung (SIR = 1.24), soft tissue sarcoma (SIR = 2.25), melanoma (SIR = 1.29), non-melanoma skin (SIR = 1.58), endometrium (SIR = 1.52), ovary (SIR = 1.48), kidney (SIR = 1.27), thyroid gland (SIR = 1.62) and leukaemia (SIR = 1.52). The excess of cancer after a breast cancer diagnosis is likely to be explained by treatment for breast cancer and by shared genetic or environmental risk factors, although the general excess of cancer suggests that there may be additional explanations such as increased surveillance and general cancer susceptibility.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".