Local metabolic rate during whole body vibration
Bibliographic record
Abstract
Whole body vibration (WBV) platforms are currently used for muscle training and rehabilitation. However, the effectiveness of WBV training remains elusive, since scientific studies vary largely in the vibration parameters used. The origin of this issue may be related to a lack in understanding of the training intensity that is imposed on individual muscles by WBV. Therefore, this study evaluates the training intensity in terms of metabolic rate of two lower-extremity muscles during WBV under different vibration parameters. Fourteen healthy male subjects were randomly exposed to 0 (control)-, 10-, 17-, and 28-Hz vibrations while standing upright on a vibration platform. A near-infrared spectrometer was used to determine the gastrocnemius medialis (GM) and vastus lateralis (VL) muscles' metabolic rates during arterial occlusion. The metabolic rates during each vibration condition were significantly higher compared with control for both muscles (P < 0.05). Each increase in vibration frequency translated into a significantly higher metabolic rate than the previous lower frequency (P < 0.05) for both muscles. The current study showed that the local metabolic rate during WBV at 28 Hz was on average 5.4 times (GM) and 3.7 times (VL) of the control metabolic rate. The substantial changes in local metabolic rate indicate that WBV may represent a significant local training stimulus for particular leg muscles.
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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 | 0.000 |
| 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) | 0.001 | 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".