Efficacy of midnight-noon Ebb-flow hour-prescription method combined with acupuncture at Baihui Bazhen acupoints in the rehabilitation of Aphasia after ischemic stroke
Bibliographic record
Abstract
Background: To investigate the clinical effectiveness of combining the midnight-noon ebb-flow hour-prescription method with acupuncture at the Baihui (GV 20) and Bazhen acupoints for rehabilitating aphasia following ischemic stroke. Methodology: 196 patients with aphasia after ischemic stroke were divided into two groups: a research group (n=98) that received language rehabilitation training along with the midnight-noon ebb-flow hour-prescription method and acupuncture at Baihui (GV 20) Bazhen acupoints, and a control group (n=98) that received only language rehabilitation training. The study recorded traditional Chinese medicine symptom scores and scores from various scales, as well as clinical efficacy. Results: The traditional Chinese medicine symptom score and National Institute of Health Stroke Scale (NIHSS) score were lower, and the scores of Stroke-Specific Quality of Life Scale (SS-QOL), Chinese Functional Communication Profile (CFCP), Montreal Cognitive Assessment (MoCA), Chinese Rehabilitation Research Center Standard Aphasia Examination (CRRCAE) were higher in the research group than in the control group after treatment. The research group also had a higher total effective rate compared to the control group. Conclusion: Midnight-noon ebb-flow hour-prescription method combined with acupuncture at Baihui (GV 20) Bazhen acupoints can effectively ameliorate NIHSS scores in patients with aphasia after ischemic stroke, facilitate language functional recovery, and improve rehabilitation.
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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) | 0.001 | 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".