Study of the efficacy of comprehensive rehabilitation of fine motor skills in patients after ischemic stroke, using hardware technology with biofeedback
Bibliographic record
Abstract
The article presents the current data on the importance of applying a multimodal approach including hardware techniques with biofeedback in restoring fine motor skills of the hand in patients after ischemic stroke in the early recovery period. The results of the combined use of peripheral magnetic stimulation (Magstim Rapid) with active training on the Hand Tutor device with biofeedback are described on the example of a clinical case. The efficiency of the complex approach was evaluated according to basic scales of neurological deficit: the National Institutes of Health Stroke Scale (NIHSS), the modified Rankin Scale, the Barthel Index for Activities of Daily Living, the Rivermead Activities of Daily Living Scale, the Montreal Cognitive Assessment Scale (MoCA). We also performed a quantitative assessment of motor deficits in the affected limb before and after application of the technique, using the MediTutor software. Based on the results of the study, a decrease in both neurological and motor deficits in the affected limb was revealed; however, further study of the effectiveness of the method with the selection of parameters and duration of therapy is necessary.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".