Early cerebral hemodynamic changes during passive movements and motor recovery after stroke.
Bibliographic record
Abstract
Recovery from hemiplegia is a complex phenomenon that depends on various adaptive processes involving both the affected and the unaffected hemisphere. Our aim in this study was to investigate changes in cerebral perfusion in hemiplegic stroke patients during passive movements and their correlation with the subsequent motor recovery. The study included 30 patients with single, subcortical ischemic cerebral lesions. Within 14 days (range 8 to 14 days) from stroke onset, all patients were examined for the effects of passive elbow movements on cerebral blood flow in the middle cerebral arteries (MCAs) by means of bilateral transcranial Doppler (TCD) ultrasonography. On the same day as TCD assessment, they were also evaluated clinically with the Canadian Neurological Scale (CNS) and with Medical Research Council (MRC) scale for motor deficit of the affected arm. A clinical evaluation using the same scales was repeated after two months of motor rehabilitation therapy. We investigated the relationship between changes of Mean Flow Velocity (MFV) during passive movements and degree of recovery after stroke. The logistic regression procedure indicated that out of all factors considered as possibly related to a good clinical motor deficit recovery of the affected arm, evaluated by means of MRC, only the MFV percentage increase played a predictive role. In particular, for each additional point of contralateral MFV percentage increase during passive movement of the affected arm, the relative probability of good clinical recovery increased 5.68 times (95% CI=1.76-18.40; p=0.004). Similar results were found when the clinical recovery was measured by means of the CNS (slope=0.40, p<0.001). Passive movements in hemiplegic stroke patients before clinical recovery elicit activation patterns that may be critical for the restoration of motor function.I n particular, early and consistent activation of the affected hemisphere, as detected with TCD, seems to predict the positive evolution of a motor deficit.
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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 | 0.000 | 0.002 |
| 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 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".