Transcutaneous electrical nerve stimulation electromagnetic interference in an implantable loop recorder
Bibliographic record
Abstract
A 61-year-old woman with severe sleep apnea, enrolled in the Reveal XT-SA study (implantable loop recorders in patients with severe sleep apnea and NO history of atrial fibrillation—AF) came to clinic for routine follow-up. An ILR (Reveal™ XT; Medtronic, Minneapolis, MN, USA) was implanted to monitor for atrial arrhythmias. A download was performed and interpreted as AF in two snapshots (Figure 1A). In closer interrogation, patient recalled using transcutaneous electrical nerve stimulation (TENS), which is a commonly used treatment for the relief of acute and chronic musculoskeletal pain. TENS has proved to interact with cardiac implantable devices.1 Careful review of 2 separate EGM episodes (Figure 1B) revealed high-frequency spikes (TENS pulses) and native QRS complexes “marching through.” These native QRS signals can be distorted during an episode of electromagnetic interference oversensing and be easily confused with fibrillatory waves.1, 2 Variations in positioning of the ILR within the chest, and oscillations produced during respiration, can also account for other reasons of ILR oversensing.3 In this case, the rapid oscillatory waves produced by TENS were oversensed by the ILR and, along with the detection of the native QRS complexes, produced an irregular detected rhythm, leading to the wrong diagnosis of true AF. Healthcare providers in charge of reading these more frequently used devices need to be aware of possible oversensing (and its sources) to avoid taking wrong medical decisions. There was no contraindication to continue with TENS in this case, and the personnel was made aware of this interaction. Authors declare no Conflict of Interests for this article.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".