Meeting the needs of the aging population: the Canadian Network on Aging and Cancer—report on the first Network meeting, 27 April 2016
Bibliographic record
Abstract
The aging of the Canadian population represents the major risk factor for a projected increase in cancer incidence in the coming decades. However, the evidence base to guide management of older adults with cancer remains extremely limited. It is thus imperative that we develop a national research agenda and establish a national collaborative network to devise joint studies that will help to accelerate the development of high-quality research, education, and clinical care and thus better address the needs of older Canadians with cancer. To begin this process, the inaugural meeting of the Canadian Network on Aging and Cancer was held in Toronto, 27 April 2016. The meeting was attended by 51 invited researchers and clinicians from across Canada, as well as by international leaders in geriatric oncology from the United States and France.The objectives of the meeting were toreview the present landscape of education, clinical care, and research in the area of cancer and aging in Canada.identify issues of high research priority in Canada within the field of cancer and aging.identify current barriers to geriatric oncology research in Canada and develop potential solutions.develop a Canadian collaborative multidisciplinary research network between investigators to improve health outcomes for older adults with cancer.learn from successful international efforts to stimulate the geriatric oncology research agenda in Canada.In the present report, we describe the education, clinical care, and research priorities that were identified at the meeting.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".