Data Mining in Cognitive Function Training of Depression Patients Applications
Bibliographic record
Abstract
Objective: To study how to use virtual reality technology (VR) to improve the cognitive function of depressive patients. This study explored the process of the diagnosis, training and evaluation of cognitive impairment in patients with depression, and provided new training methods and research ideas for improving cognitive impairment and prognosis of patients with depression. Methods: 30 mild to moderate depression subjects were randomly assigned to the experimental group, and 32 mild to moderate depression subjects were randomly assigned to the control group. The subjects in the experimental group were trained and treated with immersion virtual reality system for six months, while the subjects in the control group did not receive any treatment. MoCA scale and corresponding diagnostic criteria were used to assess the cognitive baseline level before treatment and the cognitive improvement after treatment. Result: The subjects in this study had mild to moderate depression, and the depression was similar in different genders, professions and groups. Before the cognitive training, there was no difference in the scores of MoCA scale between the experimental group and the control group, t = 0.2, P = 0.84. After the cognitive training, there was a significant difference in the scores of MoCA scale between the experimental group and the control group, t = 4.36, P = 0.00. This shows that the cognitive function training and treatment based on VR technology can effectively improve the cognitive level of depressive patients. Conclusion: Using virtual reality technology to train the cognitive ability of mild to moderate depression subjects can significantly improve the cognitive ability of the subjects, which is worthy of clinical application and promotion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".