A Longitudinal Study of the Development of Low Back Pain in an Industrial Population
Bibliographic record
Abstract
STUDY DESIGN: This is a longitudinal study in which industrial workers without chronic low back pain (LBP) were initially assessed with a comprehensive test battery and surveyed every 6 months thereafter for 2 years. OBJECTIVE: To determine factors that may predispose industrial workers who lift over 5000 kg per shift to LBP. SUMMARY OF BACKGROUND DATA: Prospective studies are small in number and often limited in breadth or depth of the test battery, methodologic issues, or investigator expertise. There are no prospective studies that focus on a homogeneous work sample of industrial employees. METHODS: Production workers (n = 149) who volunteered for the 2-year study were assessed using physical measures (e.g., muscular strength, endurance, and flexibility), lifting kinematics (a sagittal plane box lift), and health, lifestyle, and work environment data (paper questionnaires). Follow-up questionnaires were distributed every 6 months for 2 years. RESULTS: Using self-report of LBP as the main outcome measure, eight variables predicted LBP in this sample with a 75% correct prediction rate. Predictor variables included age, thoracic acceleration during the trunk velocity test, median frequency intercept of electromyography of the right L3 erector spinae, quadriceps strength, quadriceps endurance, self-assessment of fitness, having a confidante, and number of medications currently taken. CONCLUSION: Results confirmed the multifactorial nature of low back pain and suggest that personal fitness is an important defense against low back pain, even in manual handling lifting tasks.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".