Pre-hospital triage of suspected acute stroke patients in a mobile stroke unit in the rural Alberta
Bibliographic record
Abstract
Mobile Stroke Unit (MSU) expedites the delivery of intravenous thrombolysis in acute stroke patients. We further evaluated the functional outcome of patients shipped to a tertiary care centre or repatriated to local hospitals after triage by MSU in acute stroke syndrome in rural northern Alberta. Consecutive patients with suspected acute stroke syndrome were included. On the basis of neurology consultation and, Computed Tomography findings, patients, who were thrombolysed or needed advanced care were transported to the Comprehensive stroke center (CSC) (Triage to CSC group). Other patients were repatriated to local hospital care (Triage to LHC group). A total of 156 patients were assessed in MSU, 73 (46.8%) were female and the mean age was 66.6 ± 15 years. One hundred and eight (69.2%) patients, including 41 (26.3%) treated with thrombolysis were transported to the CSC (Triage to CSC group) and 48 (30.8%) were repatriated to local hospital care. The diagnosis made in MSU and final diagnosis were matching in 88% (95) and 91.7% (44, p = 0.39) in Triage to CSC and Triage to LHC groups respectively. Prehospital triage by MSU of acute stroke syndrome can reliably repatriate patients to the home hospital. The proposed model has the potential to triage patients according to their medical needs by enabling treatment in home hospitals whenever reasonable.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".