The biomechanical implications of neck position in cervical contusion animal models of SCI
Bibliographic record
Abstract
Large animal contusion models of spinal cord injury are an essential precursor to developing and evaluating treatment options for human spinal cord injury. Reducing variability in these experiments has been a recent focus as it increases the sensitivity with which treatment effects can be detected while simultaneously decreasing the number of animals required in a study. Here, we conducted a detailed review to explore if head and neck positioning in a cervical contusion model of spinal cord injury could be a factor impacting the biomechanics of a spinal cord injury, and thus, the resulting outcomes. By reviewing existing literature, we found evidence that animal head/neck positioning affects the exposed level of the spinal cord, morphology of the spinal cord, tissue mechanics and as a result the biomechanics of a cervical spinal cord injury. We posited that neck position could be a hidden factor contributing to variability. Our results indicate that neck positioning is an important factor in studying biomechanics, and that reporting these values can improve inter-study consistency and comparability and that further work needs to be done to standardize positioning for cervical spinal cord contusion injury models.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".