Study on Adjuvant Medication for Patients with Mild Cognitive Impairment Based on VR Technology and Health Education
Bibliographic record
Abstract
In order to improve the efficiency of auxiliary medication for patients with mild cognitive impairment, this paper proposes a method based on VR technology and health education. Sixty elderly patients with COPD and MCI admitted to a hospital from January 2019 to February 2020 were randomly divided into a control group and study group, with 50 cases in each group. On the basis of conventional drug therapy, health education, and respiratory muscle training, patients in the control group received routine lung rehabilitation training, while patients in the study group received lung rehabilitation training using the BioMaster virtual scene interactive rehabilitation training system. Both groups continued training for 12 weeks. Lung function indexes, 6-minute walking distance, COPD assessment test (CAT) score, and Montreal Cognitive Function Assessment Scale (MoCA) score were compared between the 2 groups before training and 4, 8, and 12 weeks after training. The experimental results show that, in the study group, the percentage of FEV1 in the predicted value at 8 weeks after training, the percentage of FEV1 in the predicted value at 12 weeks after training, and FEV1/FVC were higher than those in the control group ( P < 0.05 ). There was no significant difference in 6-minute walking distance, CAT score, and MoCA score between the two groups before training ( P > 0.05 ). Twelve weeks after training, patients in the study group had a longer 6-minute walking distance, a lower CAT score, and a higher MoCA score than those in the control group ( P < 0.05 ). It is proved that the application of virtual reality technology in lung rehabilitation training of elderly COPD patients with MCI is effective, which can effectively improve the lung function, cognitive function, and exercise tolerance of the patients and reduce the symptoms of dyspnea and the efficiency of medication.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".