Assessing gait impairment after permanent middle cerebral artery occlusion in rats using an automated computer-aided control system
Bibliographic record
Abstract
Systematic gait analyses have been widely used in clinical settings as a reliable means of evaluating stroke severity and the efficacy of rehabilitation on people. However, the extent of gait changes post-stroke in experimental quadrupeds remains to be explored. To date, gait studies in cerebral ischemia have been limited to the mild ischemia-reperfusion model. However, studies on pathophysiology and therapy of experimental stroke suggest that permanent middle cerebral artery occlusion (pMCAO) is more similar to naturally occurring cerebral ischemia in humans. This is the first preclinical study to demonstrate that pMCAO rats can be used to assess long-term functional deficits related to gait by a computer-assisted method. Our gait analysis results demonstrate obvious gait deficits in the acute phase of the disease. During recovery, gait function gradually improved, but deficits were still detectable 42 days post-pMCAO. Objective and accurate photogrammetric parameters were used to illuminate laws of impairment and compensation in rats at different stages of cerebral ischemia in injured and uninjured limbs during walking. Compared to previous gait studies involving transient (t) MCAO rats, gait changes observed in pMCAO rats were more similar to changes following naturally occurring cerebral ischemia in humans. Importantly, the average body rotation and propulsion index, not previously used, are specific parameters for accurately assessing gait function during the acute phase of post-pMCAO. Furthermore, the gait test results revealed significant correlations between the final infarction volume and earlier behavioral outcomes. In conclusion, the gait analysis is a promising tool for assessing cerebral ischemia severity, and that it may provide a new means of investigating mechanisms of cerebral ischemia and evaluating potential therapies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".