Increased risk of soft tissue sarcoma after radiotherapy in women with breast carcinoma
Bibliographic record
Abstract
BACKGROUND: Numerous studies to date have suggested an association between radiation exposure and the development of soft tissue sarcoma. The current study was performed to quantify the risk of soft tissue sarcoma in the vicinity of previously irradiated anatomic regions in women with breast carcinoma. METHODS: In this population-based, retrospective cohort study, 194,798 women who were diagnosed with invasive breast carcinoma (exclusive of those with distant metastasis) between 1973--1995 were identified, and subsequent soft tissue sarcoma cases utilizing the data from the Surveillance, Epidemiology, and End Results Program (SEER) were ascertained. Poisson regression analysis was used to calculate age standardized incidence ratios (SIR) and to model the influence of radiotherapy (RT) on the relative risk (RR) between the RT and non-RT cohorts. RESULTS: A total of 54 women in the RT cohort and 81 women in the non-RT cohort subsequently developed soft tissue sarcoma. In the RT cohort, the SIR was 26.2 (95% confidence interval [95% CI], 16.5--41.4) for angiosarcoma and was 2.5 (95% CI, 1.8--3.5) for other sarcomas; in the non-RT cohort, the SIRs were 2.1 (95% CI, 1.0--4.4) and 1.3 (95% CI, 1.0--1.7), respectively. The RT cohort demonstrated a higher risk of developing both angiosarcoma (RR: 15.9; 95% CI, 6.6--38.1) and other sarcomas (RR: 2.2; 95% CI, 1.4--3.3) compared with the non-RT cohort, and the largest increase was observed in the chest wall/breast. The elevated RR was significant even within 5 years of RT, but it reached a maximum between 5--10 years. CONCLUSIONS: The risk of soft tissue sarcoma, especially angiosarcoma, was elevated after RT in women with breast carcinoma.
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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) | 0.001 | 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".