Differences in Morphometric Measures of the Uninjured Porcine Spinal Cord and Dural Sac Predict Histological and Behavioral Outcomes after Traumatic Spinal Cord Injury
Bibliographic record
Abstract
One of the challenges associated with conducting experiments in animal models of traumatic spinal cord injury (SCI) is inducing a consistent injury with minimal variability in the degree of tissue damage and resultant behavioral and biochemical outcomes. We evaluated how the variability in morphometry of the spinal cord and surrounding cerebrospinal fluid (CSF) contributes to the variability in behavioral and histological outcomes in our porcine model of SCI. Using intraoperative ultrasound imaging, spinal cord morphometry was assessed in seven Yucatan minipigs undergoing a weight-drop T10 contusion-compression injury. Bivariate and multi-variate analysis and modeling were used to identify native morphometrical determinants of interanimal variability in histological and behavioral outcomes. The measured biomechanical impact parameters did not correlate with the histological measures or hindlimb locomotor behavior (Porcine Thoracic Injury Behavior Scale). In contrast, clear associations were revealed between CSF layer morphometry and the amount of white matter and tissue sparing. Specifically, the dorsoventral diameter of the dural sac and ventral CSF space were strong predictors of behavioral and histological outcome and together explained ≥95.0% of the variance in these parameters. In addition, a dorsoventral diameter of the spinal cord less than 5.331 mm was a strong contributing factor to poor behavioral recovery over 12 weeks. These results indicate that interanimal variability in cord morphometry provides a potential biological explanation for the observed heterogeneity in histological and behavioral outcomes. Such knowledge is helpful for appropriately balancing experimental groups, and/or varying impact parameters to match cord and CSF layer dimensions for future studies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".