The Terry Fox Research Institute’s Atlantic Dialogue on Patient-Centred Care in a Personalized Treatment World
Bibliographic record
Abstract
The words “personalized medicine” are used daily now in cancer care and research conversations. But what do those words really mean to us as patients, caregivers, physicians, managers of the health system, or researchers? Do we know how personalized medicine will affect us over the next decade? Are we prepared?Those and other questions are part of a continuing conversation that the Terry Fox Research Insti-tute is having with the Canadian public in 2010 as part of its public research and outreach project, The Pan-Canadian Dialogue Series on Cancer: Let’s Get Personal. The first dialogue was held in St. John’s, Newfoundland and Labrador, April 12, to coincide with the 30th anniversary of the Terry Fox Marathon of Hope. It featured speakers and panellists from Newfoundland and Labrador, Nova Scotia, New Brunswick, and Prince Edward Island. Three core issues framed the Atlantic discussion: cancer and population health, cancer and the health system, and the science behind cancer care.
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 | 0.030 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.021 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.021 | 0.041 |
| 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".