The Terry Fox Research Institute’s Ontario Dialogue: How Will Personalized Medicine Change Health Care?
Bibliographic record
Abstract
This is the final instalment in a series of three articles by the Terry Fox Research Institute about its pan-Canadian dialogue series, Cancer: Let’s Get Personal, a public research and outreach project undertaken in 2010. The dialogues served to launch a national and continuing conversation on personalized medicine with the medical and scientific communities and the public, including cancer survivors, patients, and caregivers. Participants at the Ontario dialogue, held in Toronto, October 18, 2010, discussed the challenges that Canadians and the health care system face as they move forward on a pathway created by advanced science and technology that will phenomenally transform cancer care and treatment. The one-size-fits-all approach to treating cancer patients is being rapidly eclipsed by an approach that treats patients and their tumours as individually as possible. As a result, a paradigm shift is occurring both in the laboratory and in the clinic, creating new approaches to conducting research and delivering treatment and care that place each and every patient—and tumour—at the centre of treatment. New approaches and practices in health care are necessary to ensure successful uptake and implementation of these advances for the benefit of all Canadians. Participating partners and supporters of the Ontario dialogue were the Ontario Institute for Cancer Research and the University Health Network.
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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 | 0.027 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.028 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 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".