The Impact of Posture on the Mechanical Properties of a Functional Spinal Unit During Cyclic Compressive Loading
Bibliographic record
Abstract
To assess how posture affects the transmission of mechanical energy up the spinal column during vibration, 18 porcine functional spinal units (FSUs) were exposed to a sinusoidal force (1500 ± 1200 N) at 5 Hz for 120 min in either a flexed, extended, or neutral posture. Force and FSU height were measured continuously throughout the collection. From these data, specimen height loss, dynamic stiffness, hysteresis, and parameters from a standard linear solid (SLS) model were determined and analyzed for differences between postures. Posture had an influence on all of these parameters. In extension, the FSU had higher dynamic stiffness values than when neutral or flexed (p < 0.0001). In flexion, the FSU had higher hysteresis than both an extended or neutral posture (p < 0.0001). Height loss was greatest in a flexed posture and smallest in an extended posture (p < 0.0001). In extension, the series spring element in the SLS model had a stiffness value higher than both flexed and neutral posture conditions, whereas the stiffness in the parallel spring was the same between extension and neutral (p < 0.01), both higher than in flexion. Viscosity coefficients were highest in extension compared to both flexed and neutral (p < 0.01). Based on these results, it was determined that posture had a significant influence in determining the mechanical properties of the spine when exposed to cyclic compressive loading.
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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 | 0.001 |
| 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".