Amusement Park Rides and Cardiac Devices: Heart Dropper or Device Stopper?
Bibliographic record
Abstract
BACKGROUND: Cardiac implantable electronic devices (CIEDs) are essential for managing cardiac conditions, but may malfunction due to magnetic fields > 10,000 mG. Roller coasters using linear induction motors (LIMs) generate magnetic fields, yet their potential for electromagnetic interference (EMI) with CIEDs is unclear. This study assesses magnetic field exposure on amusement park rides and examines healthcare provider recommendations. METHODS: Magnetic field strength was measured using gaussmeters placed at shoulder and abdomen levels, representing pediatric CIED sites. Rides at an amusement park were tested at least four times, recording median and maximum magnetic field strengths per second throughout the ride. Magnetic field strengths were compared between rides with health advisory messages (HAMs) and without (NHAMs). A survey was distributed to the Pediatric and Congenital Electrophysiology Society (PACES) and the Canadian Council of Cardiovascular Nurses to assess healthcare provider recommendations. RESULTS: A total of 15 rides were sampled: 11 with HAMs and 4 with NHAM. The mean magnetic field strength was higher for HAM rides (2.9 mG) than NHAM rides (1.6 mG; p = 0.05). Maximum field strength was also greater in HAM rides (46.4 vs. 6.5 mG; p < 0.001), and in rides using LIMs (n = 2) compared to those using other mechanisms (211.7 vs. 7.8 mG; p < 0.001). Only 18.1% (n = 13) of healthcare providers relied on published resources for amusement park ride recommendations, while 58.3% (n = 42) advised patients to consider HAMs. CONCLUSION: Magnetic field strengths on all rides were clinically insignificant, posing minimal EMI risk for CIED patients. Further validation and standardized guidelines are needed to inform healthcare recommendations for patients with CIEDs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".