Risk Factors for Low Back Pain: A Population‐Based Longitudinal Study
Bibliographic record
Abstract
OBJECTIVE: To identify risk factors for low back pain (LBP) and lumbar radicular pain and to assess whether obesity and exposure to workload factors modify the effect of leisure-time physical activity on LBP and lumbar radicular pain. METHODS: The population of this 11-year longitudinal study consists of a nationally representative sample of Finns ages ≥30 years (n = 3,505). The outcomes of the study were LBP and lumbar radicular pain for >7 days or for >30 days in the past 12 months at follow-up. RESULTS: LBP and lumbar radicular pain were more common in women than in men. LBP slightly declined with increasing age, while lumbar radicular pain increased with age. Abdominal obesity (defined by waist circumference) increased the risk of LBP (adjusted odds ratio [OR] 1.40 [95% confidence interval (95% CI) 1.16-1.68] for LBP >7 days and adjusted OR 1.41 [95% CI 1.13-1.76] for LBP >30 days) and general obesity (defined by body mass index) increased the risk of lumbar radicular pain (adjusted OR 1.44 [95% CI 1.12-1.85] for pain >7 days and adjusted OR 1.62 [95% CI 1.16-2.26] for pain >30 days). Smoking and strenuous physical work increased the risk of both LBP and lumbar radicular pain. Walking or cycling to work reduced the risk of LBP, particularly LBP for >30 days (adjusted OR 0.75 [95% CI 0.59-0.95]), with the largest reductions among nonabdominally obese individuals and among those not exposed to physical workload factors. Using vibrating tools increased the risk of lumbar radicular pain. CONCLUSION: Lifestyle and physical workload factors increase the risk of LBP and lumbar radicular pain. Walking and cycling may have preventive potential for 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".