Abstract TP246: Emergency Medical Services Survey of a Low-cost, Ambulance-based System for Mobile Neurological Assessment: The iTREAT Study
Bibliographic record
Abstract
Introduction: Modern advances in acute stroke care place an added emphasis on accurate prehospital diagnosis and triage. As part of the Improving Treatment with Rapid Evaluation of Acute Stroke via mobile Telemedicine (iTREAT) study, we assessed the EMS provider experience with a novel system for mobile telestroke assessment. Methods: We developed a 12-question survey with input from local participants in an EMS Council serving rural counties in central Virginia. Providers rated the iTREAT system on feasibility for acute stroke triage, potential effectiveness in prehospital neurological assessment, and interactions with prehospital care. All survey responses were voluntary and anonymous. Results: Since initiation of live patient enrollment, we have completed 34 ambulance-based telestroke encounters with the iTREAT system. Among 7 participating agencies, 19 EMS providers have served as tele-presenters during the telestroke assessment, and 17 EMS providers completed the voluntary survey. Of the respondents, 71% were certified EMS providers for over 5 years. Regarding technical feasibility, 69% experienced issues related to maintaining a video connection, 41% with logging in to the videoconferencing application, and 18% powering on the tablet. Of technical challenges, 41% of providers resolved the issue on their own, 18% with guidance from study staff, and 24% could not resolve the issue. Concerning patient care, 23% felt the system interfered, 35% were neutral, and 41% felt there was no interference. The majority of respondents (71%) agreed that the iTREAT system is feasible for acute stroke triage, and an effective tool (59%) for prehospital neurological assessment. In commentary, EMS participants emphasized the system’s utility in rural areas. Conclusion: This survey of the EMS experience with a low-cost, ambulance-based system for prehospital telestroke assessment reveals both technical challenges and clinical promise. Importantly, technical issues are mostly solvable in real time and correctable for further system refinement. As a novel tool for prehospital neurological assessment and acute stroke triage, the initial EMS evaluation supports further investigation of clinical efficacy, particularly in rural and underserved areas.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".