Current knowledge and future research directions in treatment-related second primary malignancies
Bibliographic record
Abstract
Currently, 17-19% of all new primary malignancies occur in survivors of cancer, causing substantial morbidity and mortality. Research has shown that cancer treatments are important contributors to second malignant neoplasm (SMN) risk. In this paper we summarise current knowledge with regard to treatment-related SMNs and provide recommendations for future research. We address the risks associated with radiotherapy and systemic treatments, modifying factors of treatment-related risks (genetic susceptibility, lifestyle) and the potential benefits of screening and interventions. Research priorities were identified during a workshop at the 2014 Cancer Survivorship Summit organised by the European Organisation for Research and Treatment of Cancer. Recently, both systemic cancer treatments and radiotherapy approaches have evolved rapidly, with the carcinogenic potential of new treatments being unknown. Also, little knowledge is available about modifying factors of treatment-associated risk, such as genetic variants and lifestyle. Therefore, large prospective studies with biobanking, high quality treatment data (radiation dose-volume, cumulative drug doses), and data on other cancer risk factors are needed. International collaboration will be essential to have adequate statistical power for such investigations. While screening for SMNs is included in several follow-up guidelines for cancer survivors, its effectiveness in this special population has not been demonstrated. Research into the pathogenesis, tumour characteristics and survival of SMNs is essential, as well as the development of interventions to reduce SMN-related morbidity and mortality. Prediction models for SMN risk are needed to inform initial treatment decisions, balancing chances of cure and SMNs and to identify high-risk subgroups of survivors eligible for screening.
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.
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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".