Stroke metrics, safety, and outcomes of telemedicine-administered thrombolytics for acute ischemic stroke: A meta-analysis
Bibliographic record
Abstract
BACKGROUND Administration of thrombolytics for acute ischemic stroke (AIS) via telemedicine has expanded in recent years at institutions without on-site neurology specialists. This helped to improve the care of stroke patients in rural areas. However, it is uncertain if telemedicine-administered thrombolytics is as safe and effective as in-person evaluation by neurology specialists. AIM The authors conducted a meta-analysis evaluating stroke metrics, safety and outcomes of telemedicine compared to in-person evaluation by neurologist specialist in AIS patients receiving intravenous thrombolytics. METHODS PubMed, EMBASE, and Cochrane were searched for randomized clinical trials and observational cohort studies. The Mantel-Haenszel method or inverse variance, as applicable, were applied to calculate an overall effect estimate for each outcome by combining specific risk ratio (RR) or standardized mean difference (SMD). Risk of bias was analyzed using the Newcastle-Ottawa Scale. Primary outcome examined was door-to-needle time (DTN). Secondary outcomes were symptomatic intracranial hemorrhage (sICH), mortality, and mRS ≤ 2. RESULTS Eleven retrospective cohort studies involving 2350 patients were included in the analysis. Of those, 34% (n = 794) received thrombolytics via telemedicine. Telemedicine was associated with a significantly longer mean DTN compared to in-person evaluation [SMD: 0.72 minutes; 95% confidence interval (CI) 0.22-1.22; P < 0.01], a similar rate of sICH [3.9% vs 4.2%; Odds ratio (OR): 0.75; 95%CI 0.42-1.37; P = 0.35], similar rate of mortality (13.2% vs 14.7%; OR: 0.87; 95%CI 0.47-1.63; P = 0.67), and comparable rate of favorable short-term functional outcome (46.8% vs 50.7%; OR: 0.79; 95%CI 0.41-1.53; P = 0.48). Risk of bias was low to moderate for each outcome. CONCLUSION The available literature suggests that telemedicine is associated with longer DTN compared to in-person evaluation. This difference in stroke metric does not affect safety or outcome. Further studies are needed to understand and address the underlying factors of the longer DTN time.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".