Effectiveness of Liu-zi-jue exercise on coronavirus disease 2019 in the patients: a randomized controlled trial.
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine the effect of Liu-zi-jue exercise on the respiratory symptoms, quality of life, and mental health of patients with mild coronavirus disease 2019 (COVID-19). METHODS: A single-center randomized controlled trial was conducted with 104 patients with mild COVID-19. The patients were randomly assigned to the Liu-zi-jue plus conventional therapy group and conventional therapy group. The outcome measures included Modified Borg Dyspnea Scale (MBDS) score, Patient Health Questionnaire-9 (PHQ-9) score, Fatigue Scale-14 (FS-14), respiratory symptoms, and vital signs. Data were collected on the first and sixth days of hospitalization and on the discharge day. RESULTS: Repeated-measures ANOVA revealed that the whole scales all showed a downward trend in the two groups (all 0.05). The results of single-group effect suggested that the whole scale score in the intervention group was significantly lower than that in the control group at the sixth day of hospitalization. Compared with the control group, only the MBDS, expectoration, and FS-14 scores showed significantly lower scores at the discharge day ( 0.001, 0.011, 0.002). Comparison within the group showed that all the variables were significantly different at the three time points with a decreasing trend ( 0.05), except for the PHQ-9 and expectoration scores ( 0.331, 0.052). All patients' vital signs remained within a stable normal range throughout the hospital stay. CONCLUSION: The results suggested that Liu-zi-jue exercise as a complementary and alternative therapy showed beneficial effects on improving the symptoms (shortness of breath, fatigue, and cough), quality of life, and mental health of patients with mild COVID-19.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| 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".