Bibliographic record
Abstract
Increasing life expectancy globally results in predictions that one in six people will be >65 years of age by 2050.Because the occurrence of most cancers is strongly associated with older age, a significant increase in the number of older adults with cancer is to be expected.It is likely that increased cancer in older adults can be explained both by the greater duration of exposure to external factors such as ultraviolet radiation, alcohol, smoking and pollution (hence modifiable by non-medical means) as well as intrinsic factors (such as metabolic stress and reactive oxygen species).These insults contribute to DNA damage and mutation that can lead to carcinogenesis if not counteracted by the appropriate repair mechanisms, or other protective strategies.Tissues from cancer-free individuals frequently contain mutations commonly observed in cancer, but these cells remain dormant until some endogenous or exogenous events promote carcinogenesis.In ageing individuals, less efficient surveillance and immune responses against cancer may represent one such event, as well as the chronic low level inflammation commonly accompanying ageing.Additionally, because of comorbidities, older patients are less robust and it is more likely that polypharmacy interferes with cancer treatment.Despite all this awareness of the impact of ageing, most cancer research, both clinical and preclinical, fails to fully consider age-associated differences in cancer occurrence and treatment, and there are very few journals specifically dedicated to publishing explorations of these issues in either the basic research or clinical context.Hence, the time has come to establish a new journal dedicated to taking a holistic approach to all aspects of cancer in older individuals.We are therefore now welcoming papers that may shed light on these increasingly important issues.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.018 |
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".