P.047 Optimizing workflow of the initial patient visit to the stroke prevention clinic
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 follow-ups. We aimed to improve the efficacy of the initial visit by increasing the completion rate of vascular imaging. Methods: Pre-post quasi-experimental study with three phases: Phase 1: Surveillance; Phase 2: Stakeholder feedback-informed intervention development (physicians and clinic staff); and Phase 3: Implementation. Interventions included a new referral order within the provincial EMR; a 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 - assessed with multivariable logistic regression Results: The study’s inclusion criteria were met by 383 patients, mean age of 67.6±13.2 years; 49% were female, 62.5% were diagnosed with vascular events. An increase in vascular imaging before the initial visit was found in Phase 3 (139/184, 75.5%) compared to Phase 1 (121/198, 61.1%, Odds ratio 1.96 95% CI 1.3-3.1; p=0.003). Fewer follow-up visits were required in Phase 3 (22.8%) compared to Phase 1 (31.8%, p=0.049). Conclusions: A uniform referral process, a standard triage process, and a nurse-led pre-visit may improve the completion of essential investigations before the patient visit.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 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".