Speed and accuracy of text-messaging emergency department electrocardiograms from a small community hospital to a provincial referral center
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Bibliographic record
Abstract
BACKGROUND: Currently, transmission of electrocardiograms (EKGs) from a small emergency department (ED) to specialists at referral hospitals can be a time-consuming and laborious process. We investigate whether text messaging by use of short message service (SMS) of EKGs from a small hospital to consultants at a large hospital is rapid and accurate. METHODS: This study involved a one-month prospective evaluation of consecutive EKGs recorded in a small community ED. Investigators obtained de-identified photographs of each EKG via a mobile phone camera. Each EKG picture, along with a brief patient clinical history, was sent via SMS to on-call emergency physicians located at a large referral care site. All images were evaluated solely on a mobile phone. The primary outcome was the proportion of SMS that were received within two minutes of being sent. As a secondary outcome, the intra-rater evaluation of the initial EKG and the SMS EKG image were compared on 13 standardized features. The tertiary outcome was cost of text messaging. RESULTS: A total of 298 patients (14.6%) had 409 EKGs performed and a total of 926 SMS were sent. 921 SMS (99.5%, 95% confidence interval (CI) 98.7-99.8%) arrived within two minutes with a median transmission time of nine seconds (interquartile range (IQR) 3-32 s). Between the gold standard original EKG, and the interpretation of the texted image, six out of 409 (1.5%, 95% CI 0.6-3.3%) had any differences recorded, across all 13 categories. Overall, the study cost 4.1 cents per texted image. CONCLUSIONS: Systematic text messaging of ED EKGs from a small community hospital to a referral center is a rapid, accurate, portable, and inexpensive method of data transfer. This may be a safe and effective strategy to communicate vital patient information.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it