Effects of Conventional Speech Therapy with Liuzijue Qigong, a Traditional Chinese Method of Breath Training, in 70 Patients with Post-Stroke Spastic Dysarthria
Bibliographic record
Abstract
BACKGROUND Post-stroke spastic dysarthria (PSSD) is a motor speech impairment that impacts patient communication and quality of life. Liuzijue Qigong (LQG), a traditional Chinese method of breath training, could serve as an effective treatment for PSSD. This study compared the effects of conventional speech therapy and conventional speech therapy combined with LQG in patients with PSSD. MATERIAL AND METHODS Seventy patients with PSSD were randomly divided into a control group (conventional speech therapy, n=35, 77.14% cerebral infarction, 22.86% cerebral hemorrhage) and experimental group (LQG combined with conventional speech therapy, n=35, 85.71% cerebral infarction, 14.29% cerebral hemorrhage). Conventional speech therapy included relaxation, breath control, organ articulation, and pronunciation training. LQG involved producing 6 different sounds (Xu, He, Hu, Si, Chui, and Xi) accompanied by breathing and body movements. Patients were treated once a day, 5 times a week, for 4 weeks. The Frenchay Dysarthria Assessment scale (FDA), speech articulation, maximum phonation time (MPT), loudness, and Montreal Cognitive Assessment scale (MoCA) were evaluated. RESULTS At 4 weeks, the experimental group showed significant improvements compared with the control group in the change of FDA (13.26±6.84 vs 18.03±5.32, P=0.028), speech articulation (63.17±22.40 vs 76.51±15.28, P=0.024), MPT (1.34±1.30 vs 3.89±3.98, P<0.001), loudness (3.46±2.74 vs 7.14±2.56, P=0.009), MoCA (19.40±3.72 vs 22.20±5.30, P=0.020), total effective rate (68.57% vs 88.57%, P=0.041). CONCLUSIONS LQG, when combined with conventional speech therapy, enhanced the comprehensive speech ability of patients with PSSD compared with conventional treatment alone.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".