Three-dimensional peak and cumulative L4/L5 spine loads and trunk postures during non-occupational tasks
Bibliographic record
Abstract
Cumulative low back loading has been shown to be a risk factor for low back pain reporting in the workplace. Evaluation of tasks outside of work might offer insight into why workers continue to have low back pain and may report pain differentially even when doing the same job. This study utilized a video-based 3D posture sampling approach to document joint postures of 18 people over a 2-hour period while performing non-repetitive tasks in and around their own homes. A 3D rigid link segment model was used to calculate reaction forces and moments at L4/L5 and joint models were used to calculate joint forces. Average peak (4.0 kN) and cumulative (9.9 MN·s) compression force estimates indicate significant loads on the low back occur during non-occupational tasks, despite the fact that participants spent most of their time (86.2%) in neutral trunk postures. Cumulative anterior reaction shear force (440 kN·s) was found to be comparable to those documented for a wide variety of occupational tasks, when extrapolated to an 8-hour shift. To our knowledge, this study is the first to include a full complement of 3D low back forces and moments, in conjunction with an assessment of trunk posture, for non-occupational activities. The evidence suggests that considering 3D peak and cumulative low back loading during non-occupational tasks is warranted and may help to explain some of the variability in the reporting of workplace-related low back disorders despite extensive ergonomic intervention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".