Getting Neurorehabilitation Right
Bibliographic record
Abstract
Animal models suggest that a month of heightened plasticity occurs in the brain after stroke, accompanied by most of the recovery from impairment. This period of peri-infarct and remote plasticity is associated with changes in excitatory/inhibitory balance and the spatial extent and activation of cortical maps and structural remodeling. The best time for experience and training to improve outcome is unclear. In animal models, very early (<5 days from onset) and intense training may lead to increased histological damage. Conversely, late rehabilitation (>30 days) is much less effective both in terms of outcome and morphological changes associated with plasticity. In clinical practice, rehabilitation after disabling stroke involves a relatively brief period of inpatient therapy that does not come close to matching intensity levels investigated in animal models and includes the training of compensatory strategies that have minimal impact on impairment. Current rehabilitation treatments have a disappointingly modest effect on impairment early or late after stroke. Translation from animal models will require the following: (1) substantial increases in the intensity and dosage of treatments offered in the first month after stroke with an emphasis on impairment; (2) combinational approaches such as noninvasive brain stimulation with robotics, based on current understanding of motor learning and brain plasticity; and (3) research that emphasizes mechanistic phase II studies over premature phase III clinical trials.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.114 | 0.073 |
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".