Sagittal Plane Kinematic Gait Analysis in C57BL/6 Mice Subjected to MOG35-55 Induced Experimental Autoimmune Encephalomyelitis
Bibliographic record
Abstract
Kinematic gait analysis in the sagittal plane has frequently been used to characterize motor deficits in multiple sclerosis (MS). We describe the application of these techniques to identify gait deficits in a mouse model of MS, known as experimental autoimmune encephalomyelitis (EAE). Paralysis and motor deficits in mice subjected to EAE are typically assessed using a clinical scoring scale. However, this scale yields only ordinal data that provides little information about the precise nature of the motor deficits. EAE disease severity has also been assessed by rotarod performance, which provides a measure of general motor coordination. By contrast, kinematic gait analysis of the hind limb in the sagittal plane generates highly precise information about how movement is impaired. To perform this procedure, reflective markers are placed on a hind limb to detect joint movement while a mouse is walking on a treadmill. Motion analysis software is used to measure movement of the markers during walking. Kinematic gait parameters are then derived from the resultant data. We show how these gait parameters can be used to quantify impaired movements of the hip, knee, and ankle joints in EAE. These techniques may be used to better understand disease mechanisms and identify potential treatments for MS and other neurodegenerative disorders that impair mobility.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".