Second Malignant Neoplasms: Assessment and Strategies for Risk Reduction
Bibliographic record
Abstract
Improvements in early detection, supportive care, and treatment have resulted in an increasing number of cancer survivors, with a current 5-year relative survival rate for all cancers combined of approximately 66.1%. For some patients, these survival advances have been offset by the long-term late effects of cancer and its treatment, with second malignant neoplasms (SMNs) comprising one of the most potentially life-threatening sequelae. The number of patients with SMNs is growing, with new SMNs now representing about one in six of all cancers reported to the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program. SMNs reflect not only the late effects of therapy but also the influence of shared etiologic factors (in particular, tobacco and excessive alcohol intake), genetic susceptibility, environmental exposures, host effects, and combinations of factors, including gene-environment interactions. For selected SMNs, risk is also modified by age at exposure and attained age. SMNs can be categorized into three major groups according to the predominant etiologic factor(s): (1) treatment-related, (2) syndromic, and (3) those due to shared etiologic exposures, although the nonexclusivity of these groups should be underscored. Here we provide an overview of SMNs in survivors of adult-onset cancer, summarizing the current, albeit limited, clinical evidence with regard to screening and prevention, with a focus on the provision of guidance for health care providers. The growing number of patients with second (and higher-order) cancers mandates that we also further probe etiologic influences and genetic variants that heighten risk, and that we better define high-risk groups for targeted preventive and interventional clinical strategies.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".