Bibliographic record
Abstract
In response to the growing gap between discovery and the optimal application of medical advancements to health care delivery, countries the world over have developed large and well funded programs to reduce these gaps. Although these programs vary in nature, they have generally largely focused more on reducing the gap in bench to bedside research. Canada's strong biomedical and patient oriented research (POR) community has a strong base from which to build, but requires support in order to fill the missing elements needed to take full advantage of the important unmet needs in health related research. In Canada, a coalition of funders of medical research, led by the Canadian Institutes for Health Research (CIHR) is developing a large and comprehensive program to build a Canadian infrastructure that will provide these missing elements, and further strengthen POR in Canada. This coalition proposes to put particular emphasis on bedside to community POR, including phase 3 clinical trials, to take advantage of and improve the sustainability of Canada's unique universal health care system. The major initiatives in POR developed by so many countries, including Canada clearly heralds a new era in clinical research, one that the Canadian research community needs to take full advantage of.
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 | 0.034 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.019 | 0.025 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 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".