Strain rate dependent behavior of the porcine spinal cord under transverse dynamic compression
Bibliographic record
Abstract
The accurate description of the mechanical properties of spinal cord tissue benefits to clinical evaluation of spinal cord injuries and is a required input for analysis tools such as finite element models. Unfortunately, available data in the literature generally relate mechanical properties of the spinal cord under quasi-static loading conditions, which is not adapted to the study of traumatic behavior, as neurological tissue adopts a viscoelastic behavior. Thus, the objective of this study is to describe mechanical properties of the spinal cord up to mechanical damage, under dynamic loading conditions. A total of 192 porcine cervical to lumbar spinal cord samples were compressed in a transverse direction. Loading conditions included ramp tests at 0.5, 5 or 50 s −1 and cyclic loading at 1, 10 or 20 Hz. Results showed that spinal cord behavior was significantly influenced by strain rate. Mechanical damage occurred at 0.64, 0.68 and 0.73 strains for 0.5, 5 or 50 s −1 loadings, respectively. Variations of behavior between the tested strain rates were explained by cyclic loading results, which revealed behavior more or less viscous depending on strain rate. Also, a parameter (stress multiplication factor) was introduced to allow transcription of a stress–strain behavior curve to different strain rates. This factor was described and was significantly different for cervical, thoracic and lumbar vertebral heights, and for the strain rates evaluated in this study.
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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 | 0.000 |
| 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".