Influence of dynamic factors on calculating cumulative low back loads
Bibliographic record
Abstract
This study examined the error induced in estimating cumulative low back loading for exposure to dynamic manual materials handling tasks by using either static or quasi-dynamic biomechanical models when compared to a dynamic model. Ten male subjects performed three sagittal plane lifting tasks at three different lifting speeds and using three different hand loads. Digitized video recordings and measured hand forces were collected in order to calculate cumulative L4/L5 spinal loading (compression, moment, joint shear, and reaction shear) using rigid link and single muscle equivalent biomechanical models. Cumulative loading was calculated using three modeling approaches: static, quasi-dynamic, and dynamic. The calculation of cumulative loading using the dynamic model was set as the "gold standard" and error in the static and quasi-dynamic approaches was determined by comparison with the dynamic model. The use of a quasi-dynamic model resulted in an average error of −2.76% across all 10 subjects, 3 tasks, 3 lifting speeds and 3 masses. The static model had an average error of −12.55%. The error in both modeling approaches was significantly effected by the type of task performed, mass lifted, speed of lift, and model variable examined indicating that neither model produced consistent errors across the lifting parameters. The small errors associated with the quasi-dynamic model indicates that it holds promise as a method to reduce the amount of data required to estimate cumulative loading yet still preserve the dynamic loading exposure of a manual materials handling task.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".