<b>Role of Back Extension Exercises in Reducing Pain and Disability Among Occupational Motorcycle Drivers With Chronic Low Back Pain</b>
Bibliographic record
Abstract
Background: Chronic low back pain (CLBP) is highly prevalent among occupational motorcycle drivers due to prolonged sitting, vibration exposure, and sustained flexed postures. Targeted rehabilitation strategies that address biomechanical deficits are essential to reducing pain and disability in this high-risk population. Objective: To evaluate the effectiveness of back extension exercises compared with Transcutaneous Electrical Nerve Stimulation (TENS), both combined with moist heat, in reducing pain intensity and functional disability among occupational motorcycle drivers with CLBP. Methods: In this randomized controlled trial, 80 male motorcycle drivers aged 20–45 years with CLBP were allocated to either a back extension exercise program or TENS for three weeks. Pain and disability were assessed at baseline and post-intervention using the Visual Analog Scale (VAS), Quebec Back Pain Disability Scale (QBDS), and Oswestry Back Disability Index (OBDI). Results: Both groups demonstrated significant improvement across all outcomes. VAS scores decreased from 6.8 to 2.36 (p<0.001), QBDS from 6.52–6.80 to 3.56–2.36 (p<0.001), and OBDI from 6.8 to 2.36 (p<0.001). Improvements exceeded minimal clinically important thresholds. Conclusion: Back extension exercises and TENS, when combined with moist heat, produce clinically meaningful reductions in pain and disability in occupational motorcycle drivers with CLBP. Structured extension-based programs represent a valuable rehabilitation strategy for this population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".