Bibliographic record
Abstract
With a stated goal of making the health care system more sustainable while delivering more effective treatments to Canadians, the federal government has announced it is investing more than $67 million in research into “personalized medicine.” The money is to be used for a research competition led by Genome Canada, in collaboration with the Canadian Institutes of Health Research (CIHR) and the Cancer Stem Cell Consortium, the government announced on January 31, 2012. The research will focus on development of treatments that can be tailored to an individual's genetic makeup and environment — what the government calls “a personalized molecular medicine approach.” This approach “…is expected to lead to better health outcomes, improved treatments and reduction in toxicity due to variable or adverse drug outcomes,” said the government in a news release. Advances in personalized medicine will help transform health care away from a “one-size-fits-all” system towards a system of “predictive, preventive and precision care,” the government said, adding that personalized medicine holds particular promise in such areas as oncology, cardiovascular diseases, neurodegenerative diseases, diabetes, pain and Alzheimer's disease.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 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".