Abstract TP250: A Substantial Rate of Neurological and Non-Neurological Stroke Mimics Are Transported by Emergency Medical Services for Acute Stroke Evaluation
Bibliographic record
Abstract
Introduction: The impact of prehospital triage of stroke mimics to designated stroke centers may be considerable, yet little information exists regarding stroke mimics in the prehospital setting. We aimed to describe the rate and clinical characteristics of neurological and non-neurological stroke mimics transported by Emergency Medical Services (EMS) to the Emergency Department (ED) for acute stroke evaluation. Methods: A retrospective, cross-sectional, observational analysis of a centralized EMS database of patients transported by EMS to the ED for suspected stroke during an 18-month period. Hospital charts and neuroimaging were utilized to determine the final diagnosis (acute stroke, stroke mimic, as well as specific underlying diagnoses). Results: A total of 960 patients were transported by EMS to the ED with suspected stroke, among whom 405 (42.2%) were stroke mimics (mean age ± SD: 66.9 ± 17.1 years; 54% male). Stroke mimics were neurological in origin in 223 (55.1%) patients and non-neurological in 182 (44.9%). Most common neurological diagnoses were seizures (n=44,19.7%), migraines (n=42,18.8%) and peripheral neuropathies (n=25, 11.2%). Most common non-neurological mimics included cardiovascular (15.9%), psychiatric (11.9%), and infectious (8.9%) diagnoses. Neurological mimics were younger (64.1 ± 17.3 years) than non-neurological mimics (70.5 ± 16.1 years, p<0.001). Median prehospital Glasgow Coma Scale scores were similar between groups (15 vs. 15, p=0.26). Mean prehospital systolic blood pressure was slightly higher in neurological (147.8±24.2 mmHg) than non-neurological mimics (141.2±26.2 mmHg, p=0.01). Conclusions: Stroke mimics represent a substantial number of patients transported by EMS for suspected stroke, with a considerable amount being non-neurological in origin. Prospective prehospital studies are warranted to help refine prehospital identification of acute stroke and thus minimize the number of stroke mimics transported by EMS for acute stroke evaluation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".