Human Postural Responses to High Vestibular Specific Extremely Low-Frequency Magnetic Stimulations
Bibliographic record
Abstract
Background: International agencies recognize the lack of knowledge to further establish standards and guidelines to protect the workers and the public from extremely low-frequency magnetic fields (ELF-MF). In that regard, postural control has been proposed as a biomarker of potential adverse effects in humans. Considering its crucial role in postural control and its specific neurophysiological characteristics, the vestibular system emerges as an ELF-MF likely target. However, postural modulation to vestibular ELF-MF exposure remains inconclusive. Previous studies led us to investigate stimulation orientation and point of application to clarify the ELF-MF impact on balance in humans. Objectives: This research aimed to investigate the acute postural impact of lateral vestibular-specific ELF-MF stimulations. Methods: Postural control of thirty eight healthy participants was analyzed with lateral vestibular-specific ELF-MF stimulations ranging from 20 Hz to 160 Hz, up to 142 T/s and vestibular electrical stimulations at the same frequencies. Both spatial orientation and quantity of movement variables were used to investigate postural modulations. Results: Despite a conclusive positive control effect, no significant effects of ELF-MF and alternating current stimulation exposures were found regardless of frequency conditions. Conclusions: Although important electric fields were generated, no postural modulation was found. However, at these frequencies, the potential vestibular activation did not translate into functional postural sway but might be observed with reflexive vestibular outcomes.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".