Practice Variation among Canadian Stroke Prevention Clinics: Pre, During, and Post-COVID-19
Bibliographic record
Abstract
BACKGROUND: Stroke is a common and serious disorder. With optimal care, 90-day recurrent stroke risk can be reduced from 10% to about 1%. Stroke prevention clinics (SPCs) can improve patient outcomes and resource allocation but lack standardization in patient management. The extent of variation in patient management among SPCs is unknown. Our aims were to assess baseline practice variation between Canadian SPCs and the impact of COVID-19 on SPC patient care. METHODS: We conducted an electronic survey of 80 SPCs across Canada from May to November 2021. SPC leads were contacted by email with up to five reminders. RESULTS: Of 80 SPCs contacted, 76 were eligible from which 38 (50.0%) responded. The majority (65.8%) of SPCs are open 5 or more days a week. Tests are more likely to be completed before the SPC visit if referrals were from clinic's own emergency department compared to other referring sources. COVID-19 had a negative impact on routine patient care including longer wait times (increased for 36.4% clinics) and higher number of patients without completed bloodwork prior to arriving for appointments (increased for 27.3% clinics). During COVID-19 pandemic, 87.9% of SPCs provided virtual care while 72.7% plan to continue with virtual care post-COVID-19 pandemic. CONCLUSION: Despite the time-sensitive nature of transient ischemic attack patient management, some SPCs in Canada are not able to see patients quickly. SPCs should endeavor to implement strategies so that they can see high-risk patients within the highest risk timeline and implement strategies to complete some tests while waiting for SPC appointment.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".