Bibliographic record
Abstract
Cancer is caused by accumulation of genetic changes which include activation of protooncogenes and loss of tumor suppressor genes. The age-specific incidence of cancer in general increases with advancing age. However, some cancers exhibit a bimodal distribution. Commonly recognized cancers with bimodal age distribution include acute lymphoblastic leukemia, osteosarcoma, Hodgkin's lymphoma, germ cell tumors and breast cancer. Delayed infection hypothesis has been used to provide explanation for the early childhood peak in leukemias and lymphomas, whereas the peak at an older age is associated with accumulation of protooncogenes and weakened immune system. Further genetic analysis and histopathological variations point to distinctly different cancers, varying genetically and histologically, which are often combined under a single category of cancers. Tumor characteristics and age distribution of these cancers varies also by population groups and has further implications on cancer screening methods. Although significant advances have been made to explain the bimodal nature of such cancers, the specific genetic mechanisms for each age distribution remain to be elucidated. Further distinction among the different cancer subtypes may lead to improvements in individual risk assessments, prevention and enhancement of treatment 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".