Eye Movement Technique to Improve Executive Function in Patients With Stroke: A Randomized Controlled Trial
Bibliographic record
Abstract
Objective: To investigate the efficacy of eye movement technique for the treatment of executive dysfunction of patients with stroke. Methods: This was a prospective, single-blinded, randomized, controlled, single-center clinical trial conducted from June 2018 to December 2019 in patients with stroke. The patients were randomized 1:1 to the routine (conventional management) and eye-move group (routine management plus eye movement technique: 5-min goal management training, 5-min computer-aided working memory, and 10 min of inhibitory control training and set conversion training). The intervention lasted 6 weeks, followed by a 4-week follow-up. The primary endpoint was the Behavioral Assessment of the Dysexecutive Syndrome (BADS) score. The secondary endpoints mainly included the Montreal Cognitive Assessment (MoCA), Wisconsin Card Sorting Test (WCST), and modified Barthel Index (MBI) scores. Results: Sixty-four patients were enrolled (32/group). After the 6-week intervention, the BADS and WCST scores of the eye-move group were significantly improved than those of the routine group (all P < 0.05), but the effects were attenuated in certain subscores after follow-up (all P > 0.05). The MoCA and MBI scores of the eye-move group were significantly higher, and the reaction time was significantly lower than those of the routine group at 4 weeks after the intervention (all P < 0.05). After follow-up, the MBI scores of the eye-move group were still higher than that of the routine group (P < 0.001), but there were no differences for MoCA scores and reaction time (both P > 0.05). Conclusion: The eye movement technique could improve the executive function of patients with stroke. These results have to be confirmed. This was a prospective, single-blinded, randomized, controlled, single-center clinical trial (ChiCTR2000036393). Clinical Trial Registration: [ www.chictr.org.cn ], identifier [ChiCTR2000036393].
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".