The association between whole body vibration exposure and spine degeneration on imaging: A systematic review
Bibliographic record
Abstract
BACKGROUND: Low frequency vibrations from motorized vehicles and heavy equipment have been associated with musculoskeletal disorders. Spine degeneration on diagnostic imaging provides direct and objective measures of the possible effects of such exposures on the spine. OBJECTIVE: The objective of this systematic review was to evaluate the association of exposure to whole-body vibration (WBV) with spine degeneration on imaging. METHODS: We conducted electronic searches in MEDLINE, CINAHL, EMBASE, and Web of Science to July 2021. Two reviewers independently screened search results, assessed quality, and extracted data. Studies evaluating the exposure to WBV and lumbar spine degeneration on imaging were included. RESULTS: Fifteen studies (16 manuscripts) were included. Seven studies including a meta-demonstrated moderate quality evidence of no association between WBV and disc degeneration. There was also moderate quality evidence of no association between WBV and disc height narrowing and osteophytes. Overall, there was low level evidence of no association between WBV and other degenerations findings. CONCLUSIONS: There was moderate to low quality evidence suggesting no association between WBV exposures with spine degeneration on imaging. The results of this study currently do not support assertion that motorized vehicle and WBV exposure accelerates degeneration and causes structural damage to the spine.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".