Risk of second cancer among women with breast cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.002 | 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 it