Using Peak and Cumulative Spinal Loading to Assess Jobs, Job Rotation and Engineering Controls
Bibliographic record
Abstract
Peak and cumulative forces on spinal structures have been identified as significant and statistically independent risk factors for reporting low back pain (LBP). This paper describes a software based approach which utilizes these risk factors to quantitatively predict the reporting of LBP by utilizing a Low Back Pain Reporting Index Score (LBPRI). Two automotive manufacturing jobs were assessed utilizing this approach and these results were utilized in the development of a specific administrative and engineering control. Analysis of the jobs with the controls in place indicated that the administrative control, job rotation, was less effective than assumed and produced an overall increase in the risk of reporting LBP. The engineering control resulted in an overall decrease in the risk of reporting LBP and this beneficial risk reduction would be delivered to any worker that performed this job. The results of this study indicate that both peak and cumulative loading must be considered in order to properly appreciate the risk of injury and the consequences associated with the implementation of administrative and engineering controls.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".