How Can You Mend a Broken Brain? - Neurorestorative Approaches to Stroke Recovery
Bibliographic record
Abstract
BACKGROUND: Stroke is a devastating disorder that strikes approximately 15 million people worldwide. While most patients survive stroke, many are left with lifelong impairments, thereby making stroke the leading cause of permanent neurological disability. Despite this, there are a few options for treatment of acute stroke. Restoration of blood flow using clot-dissolving drugs has produced impressive benefits in some patients. However, for these drugs to be effective, they must be given soon after stroke onset and relatively only a few stroke patients reach hospital within this time. Side effects of these compounds further limit their use. SUMMARY: Enhancing the brain's endogenous capacity for reorganization and self-repair offers the most promise for victims of stroke. Indeed, many stroke patients show considerable spontaneous functional improvement. Findings in the last 15 years suggest that stroke and related injury create a cerebral milieu similar to that of early brain development, a period characterized by rapid neuronal growth and neuroplasticity. A variety of interventions (e.g., stem cells, delivery of growth factors) are currently being explored in order to enhance neuroplasticity and reorganizational processes that are important for recovery of function. An emerging concept is that combinational or 'cocktail' therapies are more effective than single interventions in improving stroke recovery. Among these, one of the most promising therapies is enriched rehabilitation, a combination of environmental enrichment and task-specific therapy (e.g., reach training). KEY MESSAGES: Neurorestorative approaches to brain reorganization and repair are providing new insights into how neural circuits respond to injury and how this knowledge can be used for optimizing stroke rehabilitation practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".