Factors associated with the onset and survival of subsequent primary breast cancer in female non‐metastatic breast cancer survivors
Bibliographic record
Abstract
Abstract This study aimed to investigate the risk factors for the onset of subsequent primary breast cancer (SPBC) in women with a previous diagnosis of early‐stage breast cancer (BC) and to construct a prognostic prediction model for patients with SPBC. Using the Surveillance, Epidemiology, and End Results‐17 (SEER‐17) database, we conducted a retrospective cohort analysis on women with initial primary early‐stage BC from 2004 to 2015. Standardized incidence ratio (SIR) was calculated to determine the risk of subsequent primary cancer (SPC). A competing risk model was built to identify the risk factors for the onset of SPBC. And risk factors associated with breast cancer‐specific mortality in SPBC patients were evaluated and presented in the form of nomogram. Compared with the general population, the overall risk of SPC for all sites was significantly elevated in women with early‐stage BC (SIR = 1.21, 95% CI: 1.20–1.23), and breast is the most frequent site. Age, race and ethnicity, year of diagnosis, history of other tumors, histological type, surgery, radiation, chemotherapy, tumor size, positive lymph nodes numbers and ER status were independent risk factors (p < .05) for the onset of SPBC. A new prognosis nomogram demonstrated good discrimination after internal validation with a C‐index of 0.869 (95% CI: 0.859–0.880), and showed favorable consistency and clinical usefulness. The incidence of SPBC and prognosis of patients with SPBC were well estimated based on a large cohort. Our nomogram model had excellent prediction performance and could be a useful tool to predict prognosis.
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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 | 0.001 |
| 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 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".