Three-dimensional peak and cumulative shoulder loads and postures during non-occupational tasks: A preliminary investigation
Bibliographic record
Abstract
BACKGROUND: In order to obtain a complete understanding of the etiology of upper extremity musculoskeletal disorders, a spectrum of risk factors needs to be evaluated, within and external to the workplace. To date, cumulative shoulder loads (forces and moments) have only been documented during automotive assembly tasks. No information on shoulder loads during non-occupational tasks has been reported. OBJECTIVE: To document 3D peak and cumulative shoulder loads and postures associated with non-occupational tasks. METHODS: Seven male (35.8 ± 15.7 years) and six female (44.0 ± 14.3 years) healthy working-aged individuals volunteered for this study. A video-based 3D posture sampling approach was used to document shoulder joint postures while participants performed non-repetitive tasks in and around their own homes over a 2-hour period. A 3D rigid link segment model was used to calculate reaction forces and moments at the shoulder. RESULTS: Peak shoulder moments approached, and in some cases exceeded, published maximum isometric strength measurements, particularly in female participants. When extrapolated to a 7-hour shift, cumulative shoulder flexion and abduction moments, cumulative reaction caudal shear forces, and the time spent in non-neutral flexion and abduction were comparable in magnitude to those reported for light automotive assembly tasks. CONCLUSIONS: Non-occupational tasks should be evaluated more widely if a complete picture of the risk of musculoskeletal injury associated with shoulder loading is to be established. More work is needed to develop threshold limits for both peak and cumulative shoulder loads to improve injury prevention strategies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".