Acupuncture-Moxibustion Combined with Rehabilitation Training Is Conducive to Improving the Curative Effect, Cognitive Function, and Daily Activities of Patients with Cerebral Infarction
Post-publication record
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Bibliographic record
Abstract
Objective: To elucidate the effect of acupuncture-moxibustion combined with rehabilitation training (RHT) on the curative effect, cognitive function (CF), and activities of daily living (ADL) of patients with cerebral infarction (CI). Methods: This study enrolled 150 patients with CI admitted to the Wuhan Sixth Hospital, Affiliated Hospital of Jianghan University from June 2020 to July 2021. Among them, 80 patients who were treated with acupuncture-moxibustion combined with RHT were included in the research group, and 70 patients who received acupuncture-moxibustion alone were included in the control group. The efficacy, CF, and ADL were observed in both groups, and the influences of the two therapies on serum uric acid (UA), high-sensitivity C-reactive protein (hs-CRP), and cystatin C (Cys-C) were compared. Among the various indexes, the CF of patients was assessed by the Montreal Cognitive Assessment (MoCA), and the ADL was evaluated by the Barthel index. Results: After treatment, the research group presented significantly better efficacy, CF, and ADL than the control group, with lower levels of serum UA, hs-CRP, and Cys-C than the control group and before treatment. Conclusion: Acupuncture-moxibustion combined with RHT can inhibit serum UA, hs-CRP, and Cys-C levels of patients with CI while improving the curative effect, CF, and ADL, which is worthy of clinical promotion.
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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".