Outcomes in patients with acute stroke treated at a comprehensive stroke center using telemedicine versus in-person assessments
Bibliographic record
Abstract
INTRODUCTION: Telemedicine has been shown to be a safe and effective modality to assess and treat patients with acute stroke who present to a community hospital. There are no previous reports on using telemedicine to treat patients with acute stroke who present to a comprehensive stroke center. We report here the outcomes of patients with acute stroke treated in 2021 at our comprehensive stroke center using telemedicine versus an in-person assessment. METHODS: Patients with acute ischemic stroke who were treated after either a telemedicine or in-person assessment at our hospital in 2021 were identified by a retrospective chart review. The primary outcomes collected were door-to-needle (DTN) time for alteplase (tPA) administration, door-to-puncture (DTP) time for endovascular thrombectomy, symptomatic intracranial hemorrhage (sICH) rates and 3-month mortality. RESULTS: There were 302 patients with acute stroke treated at our hospital in 2021. Of these, 18.2% (n = 55/302) were treated using telemedicine. There were no differences in any of the outcomes between patients treated using telemedicine versus an in-person assessment: DTN (35.5 min (n = 42) vs 33 min (n = 182), p < 0.76), DTP (86.5 min (n = 30) vs 85 min (n = 134), p < 0.97), sICH (0% (n = 0/55) vs 1.6% (n = 4/245, p < 0.59) or 3-month mortality (20.6% (n = 7/34) vs 22.1% (n = 40/181), p < 0.29). DISCUSSION: To the best of our knowledge, this is the first study to report on outcomes for acute stroke patients treated using telemedicine at a comprehensive stroke center. In this study, there were no differences in outcomes between patients treated using telemedicine versus an in-person assessment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".