Variability of vibrations produced by commercial whole-body vibration platforms
Bibliographic record
Abstract
OBJECTIVE: Whole body vibration has been studied in populations experiencing neuromuscular degradation, including the elderly and individuals with neurological disorders, but methodological standardization is required to clarify its therapeutic effects. The characteristics of the vibrations actually delivered by commercial platforms are rarely measured or reported. Our objective was to quantify the vibrations (frequency, amplitude and peak acceleration) produced by several commercial platforms across different settings. METHODS: Laser and accelerometer recordings were used to measure the vibrations of 7 vibration platforms. Four loads (0 kg, 45 kg, 68 kg, 91 kg) and 3 vibration frequencies were used (30 Hz, 40 Hz, 50 Hz), totaling 12 combinations. RESULTS: In all platforms, vibration amplitude and peak acceleration varied as a function of the load used (p < 0.001 in all cases). In most platforms, the actual frequency of vibration differed from the intended frequency (actual/intended ratio ranging from 0.83 to 1.19), as a function of load and frequency. These results imply that subjects of different weights could be receiving different vibrations. CONCLUSION: Investigators should characterize and report the vibrations actually delivered in their studies, in order to increase the quality of evidence in whole body vibration studies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".