Monte Carlo Algorithm-Based Multimodal Magnetic Resonance Imaging Prognosis Prediction in Analysis of Rehabilitation Effect of Exercise Learning on Stroke Patients and Influencing Factors of Memory Function
Bibliographic record
Abstract
Based on Monte Carlo algorithm and multimodal MRI diagnosis, the effect of motor learning on motor memory function recovery in stroke patients was investigated in this research. A total of 26 stroke patients with hemiplegia treated in hospital in the past three years were recruited. Patients were rolled into routine group (13 cases) and experimental group (13 cases) according to different follow-up rehabilitation methods. All patients were treated with intravenous thrombolysis. After treatment, the conventional group received conventional rehabilitation therapy and the experimental group received restraint induced exercise therapy (CIMT). Then, T1-weighted imaging, T2-weighted imaging, 3D anatomical imaging, and resting state examinations were performed on the patients before and after treatment. All image data and image processing were performed by the Monte Carlo algorithm. Before treatment and six weeks after rehabilitation treatment, the patients’ mental state and memory function were tested using Addenbrooke’s Cognitive Examination (ACE-III) and Montreal Cognitive Assessment (MoCA). In addition, the Fugl-Meyer motor assessment, the simple test for evaluating hand function, and the modified Barthel index were used to evaluate the patient’s ability of daily living. After processing, the quality of multimode MRI image was improved obviously, and the lesion was more prominent. The fractional amplitude of low frequency fluctuation of supplement motor area in stroke patients increased after treatment combined with exercise rehabilitation ( P < 0.05 ) and ReHo decreased compared with that before treatment. The connection function of the left and right hippocampus was enhanced. The difference in ACE-III (experimental group: 16 versus 21; control group: 17.1 versus 19) scores between the two groups after treatment and before treatment was remarkable ( P < 0.05 ), but the score of patients in experimental group improved better. The MoCA (experimental group: 24.38 versus 26.47; control group: 23.13 versus 23.37) scores of the two groups of patients changed greatly from those before treatment ( P < 0.05 ), and the MoCA score ratio between the two groups was also statistically different (26.47 versus 23.37; P < 0.05 ). There was a statistical difference in the living ability of the two groups of patients before and after treatment ( P < 0.05 ). The Monte Carlo algorithm had a good processing effect on multimodal MRI images. The recovery of the experimental group was evidently better, and the difference between the two groups was substantial ( P < 0.05 ). CIMT had a good effect on the recovery of exercise rehabilitation and memory function of patients with ischemic stroke.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".