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Record W2330385479 · doi:10.2340/16501977-1868

Variability of vibrations produced by commercial whole-body vibration platforms

2014· article· en· W2330385479 on OpenAlexafffund
Milad Alizadeh-Meghrazi, José Zariffa, Kei Masani, Miloš R. Popović, B. Catharine Craven

Bibliographic record

VenueJournal of Rehabilitation Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsUniversity of Toronto
FundersToronto Rehabilitation Institute
KeywordsVibrationAccelerometerWhole body vibrationAccelerationAcousticsMechanical vibrationPhysical medicine and rehabilitationMedicineEnvironmental scienceComputer sciencePhysics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.317
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2014
Admission routes2
Has abstractyes

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