Trunk muscle forces and spinal loads during heavy deadlift: Effects of personalization, muscle wrapping, muscle lever arm, and lumbopelvic rhythm
Why this work is in the frame
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Bibliographic record
Abstract
Heavy deadlift is used as a physical fitness screening tool in the U.S. Army. Despite the relevance of such a screening tool to military tasks performed by Service Members, the biomechanical impact of heavy deadlift and its risk of low-back injury remain unknown. A kinematics-driven musculoskeletal model of spine was implemented to investigate biomechanics of the lower back in a volunteer (23 years old, height of 1.82 m, and body mass of 98.8 kg) during a 68 kg deadlift. In search of protective mechanisms, effects of model personalization and variations in trunk musculature and lumbopelvic rhythm were also investigated. The net moment, compression and shear forces at the L5-S1 reached peaks of 684 Nm, 17.2 and 4.2 kN, respectively. Geometrical personalization and changes in lumbopelvic rhythm had the least effects on predictions while increases in muscle moment arms (40%) had the largest effects that caused, respectively, 32% and 36% decrease in the maximum compressive and shearing forces. Initiating wrapping of back muscles at farther distances from the spine had opposing effects on spinal loads; peak compression at the L5-S1 decreased by 12% whereas shear increased by 19%. Despite mechanisms considered, spinal loads during heavy deadlift exceed the existing evidence concerning the threshold of injury for spinal segments, suggesting the vulnerability to injury. Chronic exposure to such high-spinal loads may lead to (micro) fractures, degeneration, pathoanatomical changes and finally low-back pain.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it