A DECADE IN THE MAKING: THE HISTORY OF NORTH AMERICA'S FIRST PAEDIATRIC STROKE PROGRAM
Bibliographic record
Abstract
Objectives: To discuss the evolution of the Paediatric Stroke Program at the Hospital for Sick Children including the successes challenges, barriers, and strategies experienced in the developmental process. The presentation will also examine future directions of the program. Methods: A ten year retrospective review of the development of the Stroke Program will illustrate how the Stroke Program evolved from a monthly clinic into a weekly clinic, expanded its complement of health care professionals, broadened its breadth of expert consultants, established a world class outcomes research program and created cutting edge hyperacute and acute stroke paediatric treatment guidelines. The presentation will also examine the exciting future initiatives such as the introduction of institutional TPA guidelines, and the work to establish a provincial paediatric stroke network. Results: The Hospital for Sick Children Paediatric Stroke Program is the first program in North America. It has led the way for research in paediatric stroke and gained world recognition as a centre of excellence in the delivery of comprehensive paediatric stroke care. Conclusion: As other major paediatric centres around the world look to develop stroke programs, they can look to the Hospital for Sick Children for its pioneering work in paediatric stroke as a model program that is worthy of duplication.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".