The association between occupational loading and spine degeneration on imaging – a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: There are inconsistencies in findings regarding the relationship of occupational loading with spinal degeneration or structural damage. Thus, a systematic review was conducted to determine the current state of knowledge on the association of occupational loading and spine degeneration on imaging. METHODS: We performed electronic searches on MEDLINE, CINAHL and EMBASE. We included cross-sectional, case control and cohort studies evaluating occupational loading as the exposure and lumbar spine structural findings on imaging as the outcomes. When possible, results were pooled. RESULTS: Seventeen studies were included in the review. Ten studies evaluated the association of occupational loading with disc degeneration (signal intensity), four of which were pooled into a meta-analysis. Of the 10 studies, only two did not identify a relationship between occupation loading and disc degeneration. A meta-analysis including four of the studies demonstrated an association between higher loading and degeneration for all spinal levels, with odds ratios between 1.6 and 3.3. Seven studies evaluated disc height narrowing and seven evaluate disc bulge, with six and five identifying an association of loading and with imaging findings respectively. Three studies evaluated modic changes and one identified and association with occupational load. CONCLUSIONS: There was moderate evidence suggesting a modest association between occupational loading and disc degeneration (signal intensity), and low-quality evidence of an association between occupational loading and disc narrowing and bulging.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".