Management of Work-Related Low Back Pain: A Population-Based Survey of Physical Therapists
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Physical therapy often is used in the management of work-related low back pain (LBP). Little information, however, is known about the types of interventions used by physical therapists in the management of this condition. The objective of this study was to describe the interventions used by physical therapists in the treatment of workers with acute or subacute LBP, with or without radiating pain below the knee. SUBJECTS: Clinical management questionnaires for workers without and with radiating pain were returned by 190 and 139 physical therapists, respectively. METHODS: For each treatment session, therapists recorded treatment objectives, interventions, and education provided to 2 workers with LBP, 1 with radiating pain and 1 without radiating pain. RESULTS: The majority of physical therapists used stretching and strengthening exercises, spinal mobilization, soft tissue mobilization and massage, manual traction, posture correction, interferential current, ultrasound, heat, and functional activities education. With radiating pain, the majority of the therapists also used cold and the McKenzie approach. Treatment objectives pursued by the majority of the therapists were decrease of pain, increase of range of motion, increase of muscle strength (force-generating capacity of muscle), decrease of muscle tension, and worker education. DISCUSSION AND CONCLUSION: Physical therapists use an array of interventions with workers with LBP. The effectiveness of most interventions reported has not been well studied.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".