Abstract WP72: Telemedicine Access, Care, And Outcomes For TIA And Minor Stroke
Bibliographic record
Abstract
Introduction: Telemedicine is increasingly used, but its effectiveness for stroke prevention after minor stroke or TIA is not known. We compared the care and outcomes in patients discharged from an emergency department (ED) with TIA or stroke before and after the implementation of telemedicine stroke prevention clinics in Ontario, Canada. We hypothesized that care and outcomes will remain similar. Methods: We used linked administrative data to identify community-dwelling adults discharged from the ED with TIA or ischemic stroke from April 2015 to March 2020 (pre-telemedicine) and April 2020 to March 2021 (post-telemedicine). We compared access to outpatient physician visits within 90 days, neuroimaging or vascular imaging within 14 days, and echocardiogram within 90 days using standardized differences (SD <0.1 indicates negligeable difference). We used Cox proportional hazard models to compare the adjusted Hazard Ratio (aHR) and 95% confidence intervals of death within 90 days pre- and post-telemedicine and cause-specific hazard models for stroke readmission with adjustment for comorbidities. Results: We identified 47,869 patients (n=40,099 pre- and n=7,770 post-telemedicine), median age 73 years [62, 82], 49% female. Baseline characteristics were similar. There was a rapid uptake in telemedicine use (Figure 1). Physician visits (92.9% vs 93.1%, SD 0.01), neuroimaging (81.3% vs 80.5%, SD 0.02), and echocardiogram use (52.5% vs 53.9% SD 0.03) were similar, but use of vascular imaging increased (74.8% vs 84.3% SD 0.24). Readmission for stroke was stable (3.9% vs 4.0%, aHR 1.00 [0.89, 1.13]), but 90-day death was higher post- compared to pre-telemedicine (2.8% vs 3.6%, aHR 1.19 [1.05, 1.36]). Conclusion: Telemedicine is a promising tool to support routine stroke prevention care. The higher mortality must be interpreted in the context of the COVID19 pandemic. Ongoing monitoring of stroke outcomes is needed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".