Augmenting the Efficacy of the Initial Patient Visit to the Stroke Prevention Clinic: A Quality Improvement Project
Bibliographic record
Abstract
Background: Referrals to the Stroke prevention clinic with incomplete preliminary investigations decrease clinic capacity due to additional workload and the need for multiple follow-ups. We aimed to improve the efficacy of the initial visit by increasing the completion rate of vascular imaging in a quality improvement (QI) project. Methods: This is a quasi-experimental study with three phases: Phase 1: Surveillance; Phase 2: Stakeholder feedback-informed intervention development (physicians and clinic staff); and Phase 3: Intervention. Interventions included a new standardized specific physician triage form listing required investigations (brain imaging, vascular imaging, cardiac tracing), and a nurse-led pre-visit via telephone. The primary outcome measure was the completion of vascular imaging by the time of visit, assessed using multivariable logistic regression adjusted for age (in years), sex, and triage category. Results: The study’s inclusion criteria were met by 397 patients, with a mean age of 67.7 ± 13.2 years; 47.8% were female, and 62.9% (250) were diagnosed with vascular events. An increase in vascular imaging before the initial visit was observed in Phase 3 (148/199, 75.5%) compared to Phase 1 (121/198, 61.1%), with an adjusted Odds ratio of 1.77 (95% CI 1.2–2.8; p = 0.01). A trend toward fewer follow-up visits was observed in Phase 3 (23.1%) compared with Phase 1 (31.8%; p = 0.052). Conclusions: Implementing a standardized triage process and a nurse-led pre-visit may improve completion of vascular imaging before patients visit the stroke prevention clinic. Further QI studies are required to improve the completion rate of rhythm monitoring in this patient group to enable early detection and management of atrial fibrillation.
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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 | 0.059 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".