Telestroke in Northern Alberta: A Two Year Experience with Remote Hospitals
Bibliographic record
Abstract
BACKGROUND: Thrombolysis in acute ischemic stroke is usually performed in comprehensive stroke centres. Lack of stroke expertise in remote small hospitals may preclude thrombolysis. Telemedicine allows such management opportunities in distant hospitals. METHODS: We report our experience in managing acute stroke over a two-year time period with telestroke. The University of Alberta Hospital acted as the 'hub' and seven remote hospitals as 'spoke'. The neurologist at the 'hub' provided stroke expertise to the local physician using either a two-way video link or telephone. Cranial CT scans were transmitted to 'hub'. Education sessions were held before the initiation of the program. RESULTS: Of 210 patients 44 (21%) received thrombolysis at the 'spoke' sites. In 34/44 (77%) two-way video link was available while in 10/44 (23%) telephone was used. Five (11.4%) patients experienced intracranial hemorrhage after thrombolysis, 2 (4.5%) were symptomatic. Favorable (mRS=0-1) outcome at three months was 16/40 (40%) and mortality was 9/40 (22.5%). Four patients were lost to follow-up. There was no significant three months outcome difference between two-way video link and telephone consultation (P = 0.689). Over two years the number of acute stroke transfers decreased from 144 to 15 at one of the 'spoke' sites, a 92.5% decline. CONCLUSION: It is possible to successfully treat patients with acute ischemic stroke at remote sites through videoconferencing or telephone consultation. Telestroke can also lead to a significant reduction in the number of patients requiring transfer to a tertiary care centre.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".