Magnetic Resonance Imaging to Visualize Stroke and Characterize Stroke Recovery: A Review
Bibliographic record
Abstract
The global burden of stroke continues to grow. Although stroke prevention strategies (e.g., medications, diet, and exercise) can contribute to risk reduction, options for acute interventions (e.g., thrombolytic therapy for ischemic stroke) are limited to the minority of patients. The remaining patients are often left with profound neurological disabilities that substantially impact quality of life, economic productivity, and increase caregiver burden. In the last decade, however, the future outlook for such patients has been tempered by movement toward the view that the brain is capable of reorganizing after injury. Many now view brain recovery after stroke as an area of scientific research with large potential for therapeutic advances, far into the future (Broderick and William, 2004). As a probe of brain anatomy, function and physiology, magnetic resonance imaging (MRI) is a non-invasive and highly versatile modality that promises to play a particularly important role in such research. Here we provide a basic review of MRI physical principles and applications for assessing stroke, looking toward the future role MRI may play in improving stroke rehabilitation methods and stroke recovery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".