Characterizing Lumbar Spine Kinematics and Kinetics During Simulated Low-Speed Rear Impact Collisions
Bibliographic record
Abstract
BACKGROUND: Recent work has demonstrated that low back pain is a common complaint following low-speed collisions. Despite frequent pain reporting, no studies involving human volunteers have been completed to examine the exposures in the lumbar spine during low-speed rear impact collisions. METHODS: Twenty-four participants were recruited and a custom-built crash sled simulated rear impact collisions, with a change in velocity of 8 km/h. Randomized collisions were completed with and without lumbar support. Inverse dynamics analyses were conducted, and outputs were used to generate estimates of peak L4/L5 joint compression and shear. RESULTS: Average (SD) peak L4/L5 compression and shear reaction forces were not significantly different without lumbar support (compression = 498.22 N [178.0 N]; shear = 302.2 N [98.5 N]) compared to with lumbar support (compression = 484.5 N [151.1 N]; shear = 291.3 N [176.8 N]). Lumbar flexion angle at the time of peak shear was 36° (12°) without and 33° (11°) with lumbar support. CONCLUSION: Overall, the estimated reaction forces were 14% and 30% of existing National Institute of Occupational Safety and Health occupational exposure limits for compression and shear during repeated lifting, respectively. Findings also demonstrate that, during a laboratory collision simulation, lumbar support does not significantly influence the total estimated L4/L5 joint reaction force.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".