Risk of Developing a Subsequent Primary Cancer among Adult Cancer Survivors
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
BACKGROUND: Improvements in cancer control have led to a drastic increase in cancer survivors who may be at an elevated risk of developing subsequent primary cancers (SPC). In this study, we assessed the risk and patterns of SPC development among 196,858 adult cancer survivors in Alberta, Canada. METHODS: We used data from the Alberta Cancer Registry to identify all first primary cancers occurring between 2004 and 2020. A SPC was considered as the next primary cancer occurring in a different site. We estimated standardized incidence ratios (SIR) for SPC development as the observed number of SPC (O) divided by the expected number of SPC (E), in which E is a weighted sum of the population-based year-age-sex-specific incidence rates and the corresponding person-years of follow-up. RESULTS: The risk of developing a SPC up to 15 years after an initial cancer was 16.2% for males and 12.2% for females. Overall, both males (SIR = 1.50) and females (SIR = 1.58) had an increased risk of a SPC. There were significant increases in SPC risk for nearly all age groups, with a greater than five-fold increase for survivors diagnosed between ages 18 and 39. Screen-detectable cancers including colorectal, lung, cervix, and breast accounted for 46% and 27% of SPC among females and males, respectively. CONCLUSIONS: Cancer survivors of nearly every initial site had substantially increased risk of a SPC, compared with the cancer risk in the general population. IMPACT: Screen-detectable cancers were common SPC sites and highlight the need to investigate optimal strategies for screening the growing population of cancer survivors.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it