Smart Meters and Routers Radiofrequency Disturbances Study with Pacemakers and Implantable Cardiac Defibrillators
Bibliographic record
Abstract
BACKGROUND: There is no scientific literature that examines radiofrequency (RF) interference from Smart Meters with cardiac implantable electronic devices (CIEDs). The objective of this in vitro study was to assess any potential interference with Medtronic CIEDs (Medtronic Inc., Minneapolis, MN, USA). METHODS: In the Quebec testing, five models of Medtronic CIEDs were placed in an acrylic cylinder filled with a saline solution and faced a Landis+Gyr Smart Meter or Router (Landis+Gyr AG, Zug, Switzerland). The distance between CIEDs and the meter casing or router antenna was 10 cm. The Meter's normal operating conditions were modified to artificially set the number of impulsions at an abnormally high level (one, two, and three impulses per second). Each scenario was repeated one to five times, for 1 minute each. In the U.S. testing, 6 cm and 15 cm (∼2.25' and ∼6') separated the six models of Medtronic CIEDs from the Schlumberger Smart Meter (Itron Inc., Liberty Lake, WA, USA), which generally sent out a 96-bit Standard Consumption Message over 3 seconds. The transmission varied in frequencies along with the interval between cycles. RESULTS: A total of 6,966 RF transmissions were completed during the 34 tests conducted in Quebec. In the United States, the CIED was exposed to the meter for 10 minutes to provide a minimum of 200 completed RF transmissions. No interference was observed in worst-case scenarios (testing of meters and CIEDs at their performance limits). CONCLUSIONS: Landis+Gyr Smart Meters/Routers and Schlumberger Smart Meters do not interfere with the functioning of the Medtronic CIEDs tested, when placed, respectively, 10 cm and 6 cm and 15 cm apart.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".