Animal models of post-ischemic forced use rehabilitation: methods, considerations, and limitations
Bibliographic record
Abstract
Many survivors of stroke experience arm impairments, which can severely impact their quality of life. Forcing use of the impaired arm appears to improve functional recovery in post-stroke hemiplegic patients, however the mechanisms underlying improved recovery remain unclear. Animal models of post-stroke rehabilitation could prove critical to investigating such mechanisms, however modeling forced use in animals has proven challenging. Potential problems associated with reported experimental models include variability between stroke methods, rehabilitation paradigms, and reported outcome measures. Herein, we provide an overview of commonly used stroke models, including advantages and disadvantages of each with respect to studying rehabilitation. We then review various forced use rehabilitation paradigms, and highlight potential difficulties and translational problems. Lastly, we discuss the variety of functional outcome measures described by experimental researchers. To conclude, we outline ongoing challenges faced by researchers, and the importance of translational communication. Many stroke patients rely critically on rehabilitation of post-stroke impairments, and continued effort toward progression of rehabilitative techniques is warranted to ensure best possible treatment of the devastating effects of stroke.
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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 | 0.018 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".