Rural Field Consultation for Remote Acute Stroke Transport Decisions
Bibliographic record
Abstract
ABSTRACT Background: The best prehospital transport strategy for patients with suspected stroke due to possible large vessel occlusion varies by jurisdiction and available resources. A foundational problem is the lack of a definitive diagnosis at the scene. Rural stroke presentations provide the most problematic triage destination decision-making. In Alberta, Canada, the implementation and 5-year experience with a rural field consultation approach to provide service to rural patients with acute stroke is described. Methods: The protocols established through the rural field consultation system and the subsequent transport patterns for suspected stroke patients during the first 5 years of implementation are presented. Outcomes are reported using home time and data are summarized using descriptive statistics. Results: From April 2017 to March 2022, 721 patients met the definition for a rural field consultation, and 601 patients were included in the analysis. Most patients ( n = 541, 90%) were transported by ground ambulance. Intravenous thrombolysis was provided for 65 (10.8%) of patients, and 106 (17.6%) underwent endovascular thrombectomy. The median time from first medical contact to arterial access was 3.2 h (range 1.3–7.6) in the direct transfers, compared to 6.5 h (range 4.6–7.9) in patients arriving indirectly to the comprehensive stroke center (CSC). Only a small proportion of patients ( n = 5, 0.8%) were routed suboptimally to a primary stroke center and then to a CSC where they underwent endovascular therapy. Conclusions: The rural field consultation system was associated with shortened delays to recanalization and demonstrated that it is feasible to improve access to acute stroke care for rural patients.
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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 | 0.004 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.001 |
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".