Effect of speech auditory feedback training on cognitive function in patients with post stroke
Bibliographic record
Abstract
Objective To observe the intervention effect of Forbrain speech auditory feedback training on cognitive dysfunction in patients with stroke. Methods 120 patients with cognitive dysfunction after stroke were divided into experimental group 1(n=40), experimental group 2 (n=40)and control group(n=40). The control group accepted conventional rehabilitation, while the experimental group 1 received Forbrain speech auditory feedback training additionally, and the experimental group 2 received the non-standard Forbrain speech auditory feedback training additionally.All the cases were assessed with Montreal cognitive assessment scale (MoCA)before and 10 weeks after intervention. Results After 10-week intervention, the scores of structure skills(2.05±0.50), attention and concentration(2.15±0.36), memory(2.18±0.59) and the total score of MoCA(17.53±2.41) in experimental group 1 were higher than those in experimental group 2 (1.80±0.46, 1.90±0.44, 1.90±0.55, 17.53±2.41) and control group(1.78±0.53), 1.85±0.36, 1.70±0.56, 17.18±2.37) (all P<0.05). The Scores of language in experimental group 1(2.03±0.48)and experimental group 2(1.85±0.53) were higher than those in control group(1.70±0.46) (all P<0.05). Conclusion Forbrain speech auditory feedback training can improve cognitive function of patients with stroke. Key words: Stroke; Cognition disorders; Forbrain speech auditory feedback training
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".