Abstract WP64: Assessment of a Smartphone App-Sensor to Assist Patients in Identification of Neurologic and Cardiac Emergencies: The Emergency Call for Heart Attack and Stroke (ECHAS) Study
Bibliographic record
Abstract
Introduction: While timely therapy of neurologic and cardiac emergencies can improve outcomes, patients often fail to identify acute events resulting in delays in seeking emergency care. ECHAS is a smartphone application and sensor system aimed to reduce patient-related delay. We performed a retrospective study to test ECHAS in identifying patients who required emergency assessment for possible myocardial infarction (MI) or stroke. Methods: We enrolled 202 patients (57 with stroke-like, 145 MI-like symptoms) who presented to the ED at a single center in Canada. Participants answered yes-no questions about their prior history, acute symptoms, and performed a finger-tapping test on an Apple iPhone with ECHAS. Answers resulted in a risk score that guided one of the three triage decisions: 1) call 911, 2) call hotline, or 3) call your primary care physician. The ground truth for the triage decision assessment was the appropriateness of the patient’s visit to the ED. Specificity could not be calculated due to low numbers of patients who did not need emergency evaluation. Results: The mean time to complete the acute assessment was 60 seconds for MI and 111 seconds for stroke. ECHAS output recommended 66% of patients call 911, 30% hotline, and 4% primary care follow-up. The sensitivity to identify the need for emergency evaluation was 0.98. The sensitivity to identify patients who were admitted for possible MI or stroke was 1.0. A negative correlation was found between low ECHAS score and prolonged symptom onset to hospital arrival time for MI-like symptoms (r = -0.21, p < 0.05). Patients found ECHAS to be very useable (Fig 1). Conclusion: The ECHAS application was highly sensitive and easily usable in guiding patients to identify medical emergencies without input by healthcare personnel. These pilot results will drive a planned 4,000-patient prospective randomized trial to evaluate whether ECHAS will reduce times from symptom onset to first medical contact for MI and stroke.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".