Bibliographic record
Abstract
The gap between existing knowledge and the patient care provided in stroke has become more apparent. The translational gap is evidence of our scientific progress, but the sheer magnitude of our implementation gap is astounding. For instance, almost 5 decades ago we recognized the risk factors for stroke, yet in Canada until recently fewer than 20% of cases of hypertension were effectively controlled. We recognize that continued exposure to risk factors will not only lead to clinically evident strokes, but far more frequently to silent strokes resulting in cognitive decline. In addition, the same risk factors cause damage to other organs. This growing gap between existing knowledge and its translational delivery is leading our politicians to demand more practical returns. As scientific and clinical opinion leaders in stroke, we have a huge opportunity now to lead the process of narrowing the translational gap. We need to keep our emphasis on individual research excellence but temper it with a social mission to improve stroke prevention, care and rehabilitation. Toward this end, the Canadian Stroke Network partnered with the Heart and Stroke Foundation of Canada to develop the 'Canadian Stroke Strategy', an approach to focus research, increase training of stroke specialists, coordinate the care of patients, and bring 'systems change' to respond to the growing gap in all facets of stroke care. Lessons learned from both successes and failures can inform our translational efficiency in the future and facilitate collective progress in stroke 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.158 | 0.051 |
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".