Trunk muscle forces and spinal loads during heavy deadlift: Effects of personalization, muscle wrapping, muscle lever arm, and lumbopelvic rhythm
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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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 | 0.000 |
| 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 | 0.000 | 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".