Assessment for occupational hazards to cardiac implantable electronic devices due to electric field exposure at power frequency within the framework of European standards
Bibliographic record
Abstract
Workers with cardiovascular implantable electronic devices (CIEDs) may face interference hazards from electromagnetic fields emitted by industrial electrical apparatus, which may compromise device functionality and safety, leading to significant occupational risks. A risk assessment approach was proposed for the non-clinical investigation in the process of specific assessing within the framework of the European standards EN 50527. An exposure system, the voltage injection system (VIS), was introduced to experimentally investigate the interference thresholds and the induced voltages of CIEDs exposed to electric fields (EFs) at power frequency (50 Hz) by in vitro testing. A thorough risk assessment was performed on four CIEDs in two exposure scenarios, as an application illustration of VIS assessment. Correspondence between in vitro testing and real-case exposure was founded based on the study of induced voltages under EF exposures to establish VIS assessment. In the risk assessment for CIEDs, severe interference was observed in some cases with maximum sensitivity and in ICDs with nominal sensitivity at the high Action Level specified in the Directive 2013/35/EU (20 kV/m). The VIS assessment proposed in this work provides an efficient, easy-to-setup, on-site solution to evaluate the EF exposures at power frequency of workers with CIEDs in the workplace. The findings highlight the importance of conducting risk assessment for specific cases.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".