Telemedicine use and outcomes after transient ischemic attack and minor stroke during the COVID-19 pandemic: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has led to an increase in telemedicine use. We compared care and outcomes in patients with transient ischemic attack (TIA) or minor ischemic stroke before and after the widespread adoption of telemedicine in Ontario, Canada, in 2020. METHODS: In a population-based cohort study using linked administrative data, we identified patients with TIA or ischemic stroke discharged from any emergency department in Ontario before the widespread use of telemedicine (Apr. 1, 2015, to Mar. 31, 2020) and after (Apr. 1, 2020, to Mar. 31, 2021). We measured care, including visits with a physician, investigations and medication renewal. We compared 90-day death before and after 2020 using Cox proportional hazards models, and we compared 90-day admission using cause-specific hazard models. RESULTS: = 11 906, 25.0%). After 2020, 83.1% of patients had 1 or more telemedicine visit within 90 days of emergency department discharge, compared with 3.8% before. The overall access to outpatient visits within 90 days remained unchanged (92.9% before v. 94.0% after; risk difference 1.1, 95% confidence interval [CI] -1.3 to 3.5). Investigations and medication renewals were unchanged. Clinical outcomes were also similar before and after 2020; the adjusted hazard ratio was 0.97 (95% CI 0.91 to 1.04) for 90-day all-cause admission, 1.06 (95% CI 0.94 to 1.20) for stroke admission and 1.07 (95% CI 0.93 to 1.24) for death. INTERPRETATION: Care and short-term outcomes after TIA or minor stroke remained stable after the widespread implementation of telemedicine during the COVID-19 pandemic. Our findings suggest that telemedicine is an effective method of health care delivery that can be complementary to in-person care for minor ischemic cerebrovascular events.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".