Creation and Evolution of the Ontario Stroke Registry: Protocol and Two Decades of Data from a Population-Based Clinical Stroke Registry
Bibliographic record
Abstract
BACKGROUND: Stroke clinical registries are critical for systems planning, quality improvement, advocacy and informing policy. We describe the methodology and evolution of the Registry of the Canadian Stroke Network/Ontario Stroke Registry in Canada. METHODS: At the launch of the registry in 2001, trained coordinators prospectively identified patients with acute stroke or transient ischemic attack (TIA) at comprehensive stroke centers across Canada and obtained consent for registry participation and follow-up interviews. From 2003 onward, patients were identified from administrative databases, and consent was waived for data collection on a sample of eligible patients across all hospitals in Ontario and in one site in Nova Scotia. In the most recent data collection cycle, consecutive eligible patients were included across Ontario, but patients with TIA and those seen in the emergency department without admission were excluded. RESULTS: Between 2001 and 2013, the registry included 110,088 patients. Only 1,237 patients had follow-up interviews, but administrative data linkages allowed for indefinite follow-up of deaths and other measures of health services utilization. After a hiatus, the registry resumed data collection in 2019, with 13,828 charts abstracted to date with a focus on intracranial vascular imaging, identification of intracranial occlusions and treatment with thrombectomy. CONCLUSION: The Registry of the Canadian Stroke Network/Ontario Stroke Registry is a large population-based clinical database that has evolved throughout the last two decades to meet contemporary stroke needs. Registry data have been used to monitor stroke quality of care and conduct outcomes research to inform policy.
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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 | 0.094 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".