Is cancer biology different in older patients?
Bibliographic record
Abstract
Roughly 50% of cancer cases occur in people aged 65 years or older. Older people are often diagnosed at a later stage and might receive less (intensive) treatment, which might affect the outcome. In addition, an older age might be associated with biological differences in tumour and microenvironment behaviour, a domain that has been poorly studied so far. In this narrative Review of published literature, we explored the reported differences in tumour biology according to age in five major cancer types: breast, colorectal, prostate, lung, and melanoma. Our literature search uncovered clear differences in tumour histology and subtype distribution in older people compared with younger patients, as well as age-specific patterns of tumour mutations and other molecular alterations. Several studies also indicate notable changes in tumour-infiltrating immune cells in tumours of older versus younger people, although this research is still in its infancy. More research is needed and might lead to a better understanding of the biology of ageing in relation to malignancy. This knowledge could provide new perspectives for more personalised cancer treatments, eventually improving the global outcomes of older patients with 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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".