Does a Smartphone‐Based ECG Recording System in Pediatric Patients With Palpitations Improve Diagnostic Yield?
Bibliographic record
Abstract
INTRODUCTION: Palpitations in children are common and obtaining symptom-rhythm correlation is diagnostic, but challenging to obtain. The AliveCor KardiaMobile monitor is a smartphone-based single-lead ECG event recorder with limited study in children. We compared using this smartphone recorder to a conventional (Cardiocall) event recorder. METHODS: We performed a prospective, randomized study of children presenting to pediatric cardiology for investigation of palpitations who require an event recorder for symptom-rhythm correlation. Patients were randomized to the smartphone or conventional recorder for rhythm documentation for 3 months or 4 weeks, respectively. Diagnostic tracings were defined as one pathologic arrhythmia or three sinus rhythm tracings. We assessed tracing quality, diagnoses obtained, and time to diagnosis between groups. Patients were surveyed to assess perceptions of using the devices. RESULTS: One hundred participants were enrolled and randomized to 50 in each group. Diagnostic tracings were achieved in 51% versus 44% (p = 0.525) in the smartphone versus conventional group at means of 23.7 (SD 36.4) versus 11.5 (SD 15.2) days, p = 0.181. Participants who used the smartphone monitor were more likely to transmit recordings (70% vs. 49%, p = 0.037) and more often willing to use the device again (87% vs. 42%, p = 0.015), with no differences between groups in finding episodes easy to record (74% vs. 100%, p = 0.15), easy to transmit (70% vs. 46%, p = 0.26), or overall satisfaction (83% vs. 58%, p = 0.13). CONCLUSION: Smartphone monitor devices provided similar diagnostic yield to conventional monitors in children. Families who used the smartphone monitor were more willing to use the device again.
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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 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".