P.035 Impact of a telestroke system on acute ischemic stroke patient outcomes and thrombolysis rates
Bibliographic record
Abstract
Background: Telestroke can improve ischemic stroke patient outcomes by improving access to physicians specialized in stroke care and increasing the rate of thrombolysis. The aim of this study to assess the effect of the newly implemented Telestroke service on ischemic stroke patient outcomes in New Brunswick, Canada, a province with a high rural population. Methods: By means of a retrospective chart review, data for 366 adult acute ischemic stroke patients (Telestroke = 15.3%; non-Telestroke = 84.7%) were collected from emergency departmentsspanning five sites in the province. Outcomes included home discharge rates, complications (i.e., hemorrhage,angioedema), mortality, rate of thrombolysis and time to treatment. Results: No significant differences emerged for home discharge rates, complications, mortality or door-to-needle time. Telestroke patients had a significantly greater rate of thrombolysis treatment (51.8% vs 6.1%) and significantly less door-to-CT time (M= 27.63 min vs M= 100.78 min) compared to the non-Telestroke group. Conclusions: Overall, both groups had similar outcomes with some trends toward improvements for patients utilizing Telestroke.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.008 | 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".