Contribution of impactor misalignment to the neurofunctional variability in porcine spinal cord contusion models
Bibliographic record
Abstract
Traumatic spinal cord lesions studies are often carried out with animal models or numerical simulations. Unfortunately, animal models usually present a high variability in severity and type of neurofunctional impairments following impact surgery. We postulate that the variability of outcomes is strongly dependent on the positioning and alignment of the impactor during the contusion. A finite elements model of the spinal cord, predicting the action potential (AP) conduction alteration, was proposed and used to perform nine numerical simulations of a 50 g weight dropped from 200 mm on the exposed spinal cord in its spinal canal. Simulations followed a 32factorial design with impactor eccentricity and spinal cord tilt angle as factors on two outcomes: injured spinal cord area (AP < 10 % of its baseline, 1h post-injury), and asymmetry of injury (ratio of right/left injured area of both half spinal cord). Eccentricity contributed highly and significantly on both outcomes, but not tilt angle. Damaged axons were found in conscious motor, sensory, and unconscious proprioception tracts. Variability in impactor alignment beyond ±6.2 % of the spinal canal width affects neurofunctional outcomes, and careful assessment of the impactor course is therefore key when producing spinal cord injury by contusion.Clinical Relevance— A precision value is proposed to mitigate the contribution of impactor misalignment to neurofunctional variability in animal models, allowing the reduction of animal used in research. The proposed method of action potential conduction assessment could easily be implanted in human numerical models for the cross-study of patient’s cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".