Post‐Stroke Hemiplegic Rodent Evaluation: A Framework for Assessing Forelimb Movement Quality Using Kinematics
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Bibliographic record
Abstract
Kinematics is the gold-standard method for measuring detailed joint motions. Recent research demonstrates that post-stroke kinematic analysis in rats reveals reaching abnormalities similar to those seen in humans after stroke. Nonetheless, behavioral neuroscientists have failed to incorporate kinematic methods for assessing movement quality in stroke models. The availability of a user-friendly method to assess multi-segment forelimb kinematics models should greatly increase uptake of this approach. Here, we present a framework for multi-segment forelimb analysis in rodents after stroke. This method greatly enhances the understanding of post-stroke forelimb motor recovery by including several movement quality metrics often used in human clinical work, such as upper-limb linear and angular kinematics, movement smoothness and kinetics, abnormal synergies, and compensations. These metrics may constitute a preclinical surrogate for the Fugl-Meyer assessment of hemiplegic patients. The data obtained using this method are 83 outputs of linear and angular kinematics and kinetics. The outputs also include 24 time series of continuous data, which afford a graphical representation of the kinematics and kinetics of the reaching cycle. We show that post-stroke rodents displayed many features resembling those seen in humans after stroke that are evident only when multi-segment kinematics models are considered. This method expands the knowledge derived from methods constrained to paw movements to a multi-segment forelimb movement quality framework. Moreover, it highlights the need for preclinical work to consider more sensitive measures of sensorimotor impairment and recovery as a means to enhance the interpretation of true recovery and compensation. © 2022 Wiley Periodicals LLC. Basic Protocol: Recording and data analysis of rodents performing the Montoya staircase task.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 it