Effect of Cognitive Rehabilitation Strategies based on Eye Movement Techniques for Executive Function of Patients after Stroke
Bibliographic record
Abstract
Objective:To observe the effect of cognitive rehabilitation strategies based on eye movement techniques for executive function of patients after stroke.Methods:A total of 64 patients with executive dysfunction after stroke from the Forth Rehabilitation Hospital of Shanghai were randomly divided into the control group and the observation group according to the Excel Rand function, with 32 cases in each group. Both groups were given routine treatment for cerebrovascular diseases. The control group received routine rehabilitation training included physical therapy, occupational therapy, swallowing and speech function training and cognitive function training, 20 min each item, 80 min each time, once a day, six days a week, continuous treatment for six weeks. The cognitive rehabilitation strategies based on eye movement techniques (20 min) were used in the observation group to replace the cognitive training part of the routine rehabilitation training, and the rest of the training was the same as the control group. The observation time of the two groups was 10 weeks, the 1st to 6th week was the intervention stage, and the 7th to 10th week was the follow-up stage. Before intervention, after intervention, after follow-up, the behavioral assessment of dysexecutive syndrome (BADS) and Wisconsin card sorting test (WCST) were used to assess executive function. Montreal cognitive assessment scale-Beijing version (MoCA-B) was used to assess cognitive function; reaction time was used to assess attention. Results: There was no statistical difference between the two groups in the BADS, WSCT, MoCA-B, reaction time before intervention (P> 0.05). ①Executive function: compared with before intervention, the BADS total score and WSCT subitems score (except for"completed classification number"of the control group) of the two groups were significantly improved after intervention (P< 0.05); the WSCT subitems score (except for"completed classification number"and"correct response number") of the two groups after follow-up were significantly improved (P< 0.05). Compared with after intervention, the BADS subitems score (such as RSCT, Total) and WSCT subitems score (such as"correct response number""wrong response number""learning to mastering") of the two groups after follow-up were significantly improved (P< 0.05). Compared with the control group, the BADS score and WSCT score (except for"learning to mastering") of the observation group after intervention were significantly improved (P< 0.05); the BADS subitems score (such as RSCT, TJT) and WSCT subitems score (such as"correct response number""wrong response number""persistent error number") of the observation group after follow-up were significantly improved (P< 0.05). ②Cognitive function: compared with before intervention, the MoCA-B score of the two groups after intervention were significantly increased (P< 0.05). Compared with after intervention, the MoCA-B score of the observation group after follow-up were significant decreased (P< 0.05). Compared with the control group, the MoCA-B score of the observation group after intervention were significantly improved (P< 0.05). ③Attention: compared with before intervention, the response time of the two groups after intervention and follow-up was significantly shorter (P< 0.05); compared with the after intervention, the response time of the two groups after follow-up was significantly prolonged (P< 0.05). Compared with the control group, the response time of the observation group after intervention was significantly shorter (P< 0.05).Conclusion:Cognitive rehabilitation strategies based on eye movement techniques can effectively improve cognitive function, attention and executive function of patients after stroke, which is worthy of clinical promotion and application.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".