Bibliographic record
Abstract
Every 10 minutes someone in Canada suffers a “brainattack, ” making stroke the most common seriousneurological condition requiring hospital admission. Each year, about 50 000 Canadians are admitted to hospital because of stroke, with an estimated cost to the health care system of $2.7 billion.1 As the management of acute is-chemic stroke advances, widespread implementation of op-timal stroke care continues to pose enormous challenges for health care systems. There are tremendous variations in the practice of care across regions, and national practice guidelines for stroke management are lacking. To achieve “best practice ” stroke care across the country, continuous surveillance of the quality of stroke care (e.g., practice au-dits with feedback) will become increasingly important. But how should quality of stroke care be defined and measured? What are the performance indicators by which hospitals and regions should be judged? It is imperative that we
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 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.042 | 0.027 |
| Insufficient payload (model declined to judge) | 0.091 | 0.040 |
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".