Geriatric Oncology: A 5-Year Strategic Plan
Bibliographic record
Abstract
The increasing rate of the older adult population across the world over the next 20 years along with significant developments in the treatment of oncology will require a more granular understanding of the older adult population with cancer. The ASCO Geriatric Oncology Community of Practice (COP) herein provides an outline for the field along three fundamental pillars: education, research, and implementation, inspired by ASCO's 5-Year Strategic Plan. Fundamental to improving the understanding of geriatric oncology is research that intentionally includes older adults with clinically meaningful data supported by grants across all career stages. The increased knowledge base that is developed should be conveyed among health care providers through core competencies for trainees and continuing education for practicing oncologists. ASCO's infrastructure can serve as a resource for fellowship programs interested in acquiring geriatric oncology content and provide recommendations on developing training pathways for fellows interested in pursuing formalized training in geriatrics. Incorporating geriatric oncology into everyday practice is challenging as each clinical setting has unique operational workflows with barriers that limit implementation of valuable geriatric tools such as Geriatric Assessment. Partnerships among experts in quality improvement from the ASCO Geriatric Oncology COP, the Cancer and Aging Research Group, and ASCO's Quality Training Program can provide one such venue for implementation of geriatric oncology through a structured support mechanism. The field of geriatric oncology must continue to find innovative strategies using existing resources and partnerships to address the pressing needs of the older adult population with cancer to improve patient 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 | 0.019 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".