Efficacy and Safety of the Traditional Herbal Medicine, <i>Gamiguibi</i> -tang, in Patients With Cancer-Related Sleep Disturbance: A Prospective, Randomized, Wait-List-Controlled, Pilot Study
Bibliographic record
Abstract
BACKGROUND: Sleep disturbance is the second most bothersome symptom in patients with cancer, and it can significantly impair their quality of life. The aim of this study was to investigate the efficacy and safety of the traditional herbal medicine Gamiguibi-tang (GGBT) in patients with cancer-related sleep disturbance. METHODS: We conducted a prospective, randomized, wait-list-controlled, open-label pilot clinical trial on cancer-related sleep disturbance. Patients with cancer experiencing poor sleep quality with a Pittsburgh Sleep Quality Index of at least 6 were randomly assigned to the GGBT and wait-list groups to receive GGBT and conventional care, respectively, for 2 weeks. The primary endpoint was the Insomnia Severity Index (ISI) score. Fatigue, depression, and cognitive impairment were assessed as the secondary endpoints by using the Brief Fatigue Inventory (BFI), Beck Depression Inventory (BDI), and Montreal Cognitive Assessment (MoCA). RESULTS: Thirty participants who met the eligibility criteria were enrolled. Sleep disturbance assessed using the ISI improved significantly more in the GGBT group than in the wait-list group (-5.5 ± 4.4 vs 0.1 ± 1.1, P < .001). Fatigue level determined using the BFI also improved significantly more in the GGBT group than in the wait-list group (-0.8 ± 0.8 vs 0.0 ± 0.3, P = .002). The BDI and MoCA scores showed no significant changes. Adverse events were reported in two patients in the GGBT group and consisted of mild dyspepsia and mild edema. CONCLUSION: GGBT may be a potential treatment option for cancer-related sleep disturbance. Further research is needed to investigate the efficacy and safety of GGBT.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".