Comparison between high-velocity low-amplitude manipulation and muscle energy technique on pain and trunk neuromuscular postural control in male workers with chronic low back pain: A randomised crossover trial
Bibliographic record
Abstract
BACKGROUND: A therapeutic recommendation for restoring function in individuals with chronic low back pain (CLBP) is manual therapy through manipulative spinal or muscle energy techniques. OBJECTIVES: To compare the effectiveness of two osteopathic manipulative techniques on clinical low back symptoms and trunk neuromuscular postural control in male workers with CLBP. METHOD: Ten male workers with CLBP were randomly allocated to two groups: high-velocity low-amplitude (HVLA) manipulation or muscle energy techniques (MET). Each group received one therapy per week for both techniques during 7 weeks of treatment. Pain and function were measured by using the Numeric Pain-Rating Scale, the McGill Pain Questionnaire and the Roland Morris Disability Questionnaire. The lumbar flexibility was assessed by Modified Schober Test. Electromyography (EMG) and force platform measurements were used for evaluation of trunk muscular activation and postural balance, respectively at three different times: baseline, post intervention, and 15 days later. RESULTS: < 0.01) in reducing pain with large clinical differences (-1.8 to -2.8) across immediate and after 15 days. However, no significant effect between groups and times was found for other variables, namely neuromuscular activation and postural balance measures. CONCLUSION: Both techniques (HVLA thrust manipulation and MET) were effective in reducing back pain immediately and 15 days later. Neither technique changed the trunk neuromuscular activation patterns nor postural balance in male workers with LBP. CLINICAL IMPLICATIONS: These results may facilitate clinical decision-making for CLBP management in physiotherapy programs.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".