Geometrical variations in white and gray matter affect the biomechanics of spinal cord injuries more than the arachnoid space
Bibliographic record
Abstract
Traumatic spinal cord contusions lead to loss of quality of life, but their pathomechanisms are not fully understood. Previous studies have underlined the contribution of the cerebrospinal fluid in spinal cord protection. However, it remains unclear how important the contribution of the cerebrospinal fluid is relative to other factors such as the white/gray matter ratio. A finite element model of the spinal cord and surrounding morphologic features was used to investigate the spinal cord contusion mechanisms, considering subarachnoid space and white/gray matter ratio. Two vertebral segments (T6 and L1) were impacted transversely at 4.5 m s −1 , which demonstrated three major results: While the presence of cerebrospinal fluid plays a significant contributory role in spinal cord protection (compression percentage decreased by up to 19%), the arachnoid space variation along the spine appears to have a limited (3% compression decrease) impact. Differences in the white and gray matter geometries from lumbar to thoracic spine levels decrease spinal cord compression by up to 14% at the thoracic level. Stress distribution in the sagittal spinal cord section was consistent with central cord syndrome, and local stress concentration on the anterior part of the spinal cord being highly reduced by the presence of cerebrospinal fluid. The use of a refined spinal cord finite element method showed that all the geometrical parameters are involved in the spinal cord contusion mechanisms. Hence, spinal cord injury criteria must be considered at each vertebral level.
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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) | 0.001 | 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".