Back Belt Use for Prevention of Occupational Low Back Pain: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Back pain continues to be the leading overall cause of morbidity and lost productivity in the workplace. Recently, there has been a renewed interest in the use of back belts by industry to reduce occupational low back pain (LBP). OBJECTIVES: To examine the literature and evaluate the effectiveness of back belt use for the primary prevention of occupational LBP. METHODS: MEDLINE, CINAHL, EMBASE, and HEALTHSTAR were searched for relevant articles published up to July 2003. Studies were included if participants were material handlers, and outcomes included the incidence and/or duration of lost time of reported LBP among workers who wore back belts compared with those who did not. The quality of the evidence was scored independently by 2 reviewers using a double rating method, first according to research design followed by an internal validity rating. Final synthesis of the evidence was performed in which the evidence was classified as good, fair, conflicting, or insufficient. RESULTS: Ten epidemiologic studies meeting inclusion criteria were identified. Of 5 randomized controlled trials, 3 showed no positive results with back belt use; 2 cohort studies had conflicting results; and 2 nonrandomized controlled studies and 1 survey showed positive results. CONCLUSIONS: Currently, because of conflicting evidence and the absence of high-quality trials, there is no conclusive evidence to support back belt use to prevent or reduce lost time from occupational LBP.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".