Mobile stroke unit triage of patients with a suspected stroke: a novel solution to reducing suspected stroke admissions in busy emergency departments
Bibliographic record
Abstract
Background Evaluation of patients with a suspected stroke is one of the most common neurological emergencies requiring rapid, comprehensive assessment by the stroke service to determine patient eligibility for timely reperfusion therapies. Prehospital evaluation may help to improve patient selection and reduce avoidable admissions to overcapacity emergency departments. Methods and results We report on our early experience of prehospital triage of patients with a suspected stroke using a mobile stroke unit (MSU) equipped with CT scanner in rural Alberta. During the initial 4 months of operation, 28 patients were evaluated by the team in the MSU. Eight patients were within the time window of thrombolysis and were treated with intravenous tissue plasminogen activator in the MSU. No patients suffered haemorrhage or any other complications. Fourteen patients with multiple aetiologies (stroke mimics 6, transient ischaemic attacks 2, subacute stroke outside thrombolysis window 2, intracranial haemorrhage 3 and cerebral contusion 1) were assessed in the field and transferred to the tertiary hospital. Six patients after assessment and imaging were repatriated back to the local hospital as they were deemed stroke mimics or were outside of the reperfusion window. Conclusions The MSU offers a novel approach to performing timely evaluation and treatment of patients with a suspected stroke in rural settings and may help reduce admissions to overcapacity tertiary care facilities.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".