Trajectories of Stroke Care in Ontario: Which Path to Best Care?
Bibliographic record
Abstract
BACKGROUND: Few studies have tracked stroke survivors through transitions across the health system and identified the most common trajectories and quality of care received. The objectives of our study were to examine the trajectories that incident stroke patients experience and to quantify the extent to which their care adhered to the best practices for stroke care. METHODS: A population-based cohort of first-ever stroke/transient ischemic attack (TIA) patients from the 2012/13 Ontario Stroke Audit was linked to administrative databases using an encrypted health card number to identify dominant trajectories (N=12,362). All trajectories began in the emergency department (ED) and were defined by the transitions that followed immediately after the ED. Quality indicators were calculated to quantify best practice adherence within trajectories. RESULTS: Six trajectories of stroke care were identified with significant variability in patient characteristics and quality of care received. Almost two-thirds (64.5%) required hospital admission. Trajectories that only involved the ED had the lowest rates of brain and carotid artery imaging (91.5 and 44.2%, respectively). Less than 20% of patients in trajectories involving hospital admissions received care on a stroke unit. The trajectory involving inpatient rehabilitation received suboptimal secondary prevention measures. CONCLUSIONS: There are six main trajectories stroke patients follow, and adherence to best practices varies by trajectory. Trajectories resulting in patients being transitioned to home care following ED management only are least likely and those including inpatient rehabilitation are most likely to receive stroke best practices. Increased time in facility-based care results in greater access to best practices. Stroke patients receiving only ED care require closer follow-up by stroke specialists.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".