Electromagnetic Interference of Communication Devices on ECG Machines
Bibliographic record
Abstract
BACKGROUND: Use of communication devices in the hospital environment remains controversial. Electromagnetic interference (EMI) can affect different medical devices. Potential sources for EMI on ECG machines were systematically tested. HYPOTHESIS: Communication devices produce EMI on ECG machines. EMI impairs ECG interpretation. METHODS: The communication devices tested were: a global system for mobile communication (GSM) receiver, a code division multiple access (CDMA) receiver, an analog phone, a wireless local area network, and an alpha-numeric pager. EMI was tested on 3 ECG machines: MAC 5000, MAC 1200, and ELI 100. The devices were tested at 2 and 1 meter, 50, 25, and 0 cm from the acquisition module. The ECGs were presented to a heterogeneous group of clinical providers, (medical students, residents, nurses, industry representatives from cardiac devices companies, and attending cardiologists) to evaluate the impact of EMI on ECG interpretation skills. RESULTS: EMI was detected on the MAC 5000 ECG machine when activated GSM, CDMA, and analog phones were placed on top of the acquisition module. No EMI was seen on the other ECG machines or when phones were at a longer distance or deactivated. EMI was incorrectly diagnosed in 18% of the cases. EMI was confused most frequently with atrial fibrillation or flutter (52%), ventricular arrhythmias (22%), and pacemaker dysfunction (26%). Medical students (p < 0.003) and non-cardiology residents (p = 0.05) demonstrated significantly worse performance on EMI interpretation. CONCLUSIONS: Digital and analog phones produce EMI on modern ECG machines when activated in direct contact to the acquisition module. EMI impairs ECG interpretation.
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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 | 0.001 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".