Whole-Body Vibration to Treat Low Back Pain: Fact or Fad?
Bibliographic record
Abstract
PURPOSE: The purpose of this systematic review was to evaluate the current evidence base for whole-body vibration as a treatment for low back pain (LBP). SUMMARY OF KEY POINTS: Whole-body vibration through occupational exposure has previously been recognized as an aetiological factor in LBP. Previous studies have identified whole-body vibration (WBV) as a cause of LBP in various sitting-based occupations that involve machinery and repetitive vibration. In the last decade, however, WBV has been advocated as a safe and effective treatment for LBP. Despite the growing popularity of WBV in clinical practice, this systematic review of the literature identified only two studies that investigated the effectiveness of WBV as a treatment option for LBP, and an assessment of the quality of these studies demonstrated several methodological problems that may have biased their findings. While there is emerging evidence for the effectiveness of WBV in treating some medical conditions, the evidence for WBV as a treatment for LBP remains equivocal. RECOMMENDATIONS: Based on the current body of evidence, routine use of WBV to treat LBP should be undertaken with caution. Further rigorous research designed to investigate the effectiveness of WBV as a safe and high-quality treatment for LBP is required.
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 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 | 0.024 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".