Abstract TP44: No Differences In Outcomes For Stroke Patients Presenting To A Comprehensive Stroke Center And Treated Using Telestroke Vs. In-person Assessments During The Covid-19 Pandemic.
Bibliographic record
Abstract
Introduction: Telestroke is the use of videoconferencing technology by a stroke specialist to assess and treat acute ischemic stroke patients who present elsewhere. It has been well-studied previously in a “Hub-and-Spoke” model. In 2021, Telestroke was used to assess and treat acute ischemic stroke patients who presented primarily after-hours to our comprehensive stroke center because we were concerned the COVID-19 pandemic could affect the timeliness of in-person code stroke assessments. After implementation, we determined the efficacy and safety outcomes for patients treated with Telestroke versus in-person assessments. Hypothesis: We hypothesized that there will be no difference in the efficacy or safety outcomes between patients treated using Telestroke vs. in-person assessments. Methods: A retrospective chart review identified acute ischemic stroke patients who presented to our center in 2021, who were assessed and treated using either Telestroke or an in-person assessment. The primary outcomes for efficacy were door-to-needle (DTN) time for alteplase administration and door to puncture (DTP) time for endovascular thrombectomy. The primary safety outcomes were 3-month mortality and symptomatic intracranial hemorrhage rates (sICH). Results: We treated 302 acute stroke patients in 2021, with 18.2% (n=55/302) of patients treated using Telestroke. There were no differences in clinical outcomes between patients treated using Telestroke vs. in-person assessments: median DTN (35.5min (n=42) vs. 33min (n=182), p<0.76), median DTP (86.5min (n=30) vs. 85min (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). Conclusion: This is the first study to our knowledge that reports on using Telestroke at a comprehensive stroke center. In this study, there were no differences in the clinical outcomes between stroke patients treated with Telestroke vs. in-person assessments. These results support using Telestroke to meet the increasing demands for acute stroke assessments at other comprehensive stroke centers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".