Comparison of six degree-of-freedom pure moment and non-uniform, physiologically-derived spinal loading at quasi-static and dynamic rates using a six-axis joint motion simulator
Bibliographic record
Abstract
Current in vitro testing methods often fail to replicate the complex physiological loads present in the lumbar spine. The goal of this study was to develop and evaluate a novel approach to experimentally simulate non-uniform, physiologically-derived, six degree-of-freedom (6DOF) spinal loading in comparison to pure moment testing using a six-axis joint motion simulator (AMTI VIVO) at quasi-static and dynamic rates. Physiologically-derived 6DOF loading waveforms were developed from the Orthoload dataset, averaging three movement types (flexion-extension, lateral bending and axial rotation) across the reported subjects. Segmental range of motion (ROM) was measured during the simulated movements to compare the effects of loading rate (quasi-static, 0.5Nm/s vs. dynamic, 5Nm/s) and waveform type (pure moment vs. physiologically-derived 6DOF force control). Eight fresh-frozen cadaveric lumbar spine segments (four L2/L3, four L4/L5) from four donors (69 ± 4.7 years; 3 female, 1 male) were used. ROM was significantly greater under pure moment loading than physiologically-derived 6DOF loading protocols at both quasi-static and dynamic rates. Dynamic loading led to reduced ROM in pure moment and physiologically-derived tests compared to quasi-static rates. The findings from this study highlight a new potential approach to apply non-uniform 6DOF spinal loading waveforms using the VIVO joint motion simulator. Further, novel use of this system enabled dynamic ROM from 6DOF load control waveforms at physiologic loading rates (5Nm/s). Ultimately, the development and comparison of the different spinal loading conditions conducted in this study provides further advocacy for more comprehensive in vitro testing to understand lumbar spinal biomechanics.
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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) | 0.000 | 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".