Cancer Survivorship—Genetic Susceptibility and Second Primary Cancers: Research Strategies and Recommendations
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
Cancer survivors constitute 3.5% of the United States population, but second primary malignancies among this high-risk group now account for 16% of all cancer incidence. Although few data currently exist regarding the molecular mechanisms for second primary cancers and other late outcomes after cancer treatment, the careful measurement and documentation of potentially carcinogenic treatments (chemotherapy and radiotherapy) provide a unique platform for in vivo research on gene-environment interactions in human carcinogenesis. We review research priorities identified during a National Cancer Institute (NCI)-sponsored workshop entitled "Cancer Survivorship--Genetic Susceptibility and Second Primary Cancers." These priorities include 1) development of a national research infrastructure for studies of cancer survivorship; 2) creation of a coordinated system for biospecimen collection; 3) development of new technology, bioinformatics, and biomarkers; 4) design of new epidemiologic methods; and 5) development of evidence-based clinical practice guidelines. Many of the infrastructure resources and design strategies that would facilitate research in this area also provide a foundation for the study of other important nonneoplastic late effects of treatment and psychosocial concerns among cancer survivors. These research areas warrant high priority to promote NCI's goal of eliminating pain and suffering related to cancer.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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