Improving Care for Older Adults with Cancer in Canada: A Call to Action
Bibliographic record
Abstract
Most patients diagnosed with and dying from cancer in Canada are older adults, with aging contributing to the large projected growth in cancer incidence. Older adults with cancer have unique needs, and on a global scale increasing efforts have been made to address recognized gaps in their cancer care. However, in Canada, geriatric oncology remains a new and developing field. There is increasing recognition of the value of geriatric oncology and there is a growing number of healthcare providers interested in developing the field. While there is an increasing number of dedicated programs in geriatric oncology, they remain limited overall. Developing novel methods to delivery geriatric care in the oncology setting and improving visibility is important. Formal incorporation of a geriatric oncology curriculum into training is critical to both improve knowledge and demonstrate its value to healthcare providers. Although a robust group of dedicated researchers exist, increased collaboration is needed to capitalize on existing expertise. Dedicated funding is critical to promoting clinical programs, research, and training new clinicians and leaders in the field. By addressing challenges and capitalizing on opportunities for improvement, Canada can better meet the unique needs of its aging population with cancer and ultimately improve their outcomes.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".