Restricting lumbar spine flexion redistributes and changes total mechanical energy expenditure during lifting
Bibliographic record
Abstract
Minimizing lumbar spine flexion during lifting requires greater lower extremity joint motion. However, the effects of these kinematic changes on lumbar and lower extremity joint kinetics are unknown. Further, it is unclear whether the distribution of biomechanical demands throughout the lumbar spine and lower extremity during lumbar spine flexion restricted lifting are modulated by task factors like lift origin height and object mass. This study examined the influence of restricting lumbar spine flexion during lifting on the distribution of biomechanical demands, operationalized as mechanical energy expenditure (MEE), across the lumbar spine and lower extremity joints during lifting tasks. Twenty participants performed a series of lifting tasks that varied by lift origin height, object mass and presence or absence of lumbar spine motion restricting harness. MEE was quantified for the lumbar spine and lower extremity joints and summed across all joints to represent the total MEE. Distributions of MEE were compared across combinations of the three task factors. Total MEE was greater when lifting with restricted spine motion (p < 0.001). MEE was redistributed away from the lumbar spine and predominantly to the hips in the spine restricted conditions (p < 0.001). The nature and magnitude of this effect was modulated by lift origin height for the lumbar spine (p < 0.001) and hips (p < 0.001). Findings demonstrated that biomechanical demands can be shifted from the lumbar spine to the lower extremity when lifting with restricted spine flexion, which might help mitigate overuse injuries through coordinative variability.
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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 | 0.001 |
| 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".