Sagittal plane lumbar loading when navigating an obstacle and carrying a load
Bibliographic record
Abstract
The current study quantified lumbar loading while carrying an anterior load mass and navigating an obstacle. Eight healthy male participants walked down a walkway and crossed an obstacle under three randomised LOAD conditions; empty-box (2 KG), five kilogram (5 KG) and ten kilogram (10 KG). Each walk was assessed at two events: left foot mid-stance (LMS) and right toe-crossing (TC) to characterise any changes from approach to crossing. Measures of interest included: trunk pitch, L4/L5 joint moment, compression, joint anterior-posterior shear and erector spinae activation. Findings demonstrate that obstacle crossing extended posture by 50, 41, 44%, respectively for each carried load magnitude. Further, these results indicate that shear rather than compressive loading may be an important consideration during crossing due to increase by 8, 9, 22% from LMS to TC for each load magnitude tested. These results provide insight into sagittal lumbar loading when navigating an obstacle while carrying a load. Practitioner Summary: The risk of carrying while navigating obstacles on the lumbar spine is not completely understood. The forces at the lumbar spine while simultaneously carrying and obstacle crossing were analysed. Data indicate that carrying and obstacle crossing influence lumbar shear loads, thereby moderately increasing the relative risk at lumbar spine.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".