Validation of occupational estimates of cumulative low-back load
Bibliographic record
Abstract
In most genuine industrial settings, it is not yet feasible to directly measure in vivo tissue loads, nor is it practical to estimate dynamic load-time histories using biomechanical models. Thus, data extrapolation techniques are often used for obtaining occupational estimates of shift or daily cumulative low-back load exposures. These techniques are reliant on the assumption that the observed duty cycle of apparently stereotypical work is consistent over long working durations. This investigation evaluated the validity of this assumption using a controlled laboratory-based repetitive lifting task. Nine men performed 30-minutes of sagittal plane repetitive lifting tasks. Upper body kinematics were captured during the tasks, and a two-dimensional dynamic biomechanical model was used to generate peak and cumulative estimates of low-back loads. Over the course of the 30-minute testing sessions, kinematic adaptations at the elbow were responsible for an 8% reduction in duty cycle duration while peak low-back load magnitudes remained consistent. Combining reductions in duty cycle duration with negligible changes in peak loading contributed to a small decrease (⩽ 10%) in cumulative low-back load over the final 20-minutes of lifting. However, when data extrapolation was incorporated to estimate a shift exposure it was found that these changes could overestimate occupational cumulative low-back loading exposures by 10–27% inferences made regarding the risk of low-back pain or injury reporting associated with exposure.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".