Effectiveness of Chinese Herbal Medicine as a Complementary Treatment for Neutropenia Prevention and Immunity Modulation During Chemotherapy in Patients With Breast Cancer: Protocol for a Real-World Pragmatic Clinical Trial
Bibliographic record
Abstract
BACKGROUND: In recent years, advancements in cancer treatment have enabled cancer cell inhibition, leading to improved patient outcomes. However, the side effects of chemotherapy, especially leukopenia, impact patients' ability to tolerate their treatments and affect their quality of life. Traditional Chinese medicine is thought to provide complementary cancer treatment to improve the quality of life and prolong survival time among patients with cancer. OBJECTIVE: This study aims to evaluate the effectiveness of Chinese herbal medicine (CHM) as a complementary treatment for neutropenia prevention and immunity modulation during chemotherapy in patients with breast cancer. METHODS: We will conduct a real-world pragmatic clinical trial to evaluate the effectiveness of CHM as a supplementary therapy to prevent neutropenia in patients with breast cancer undergoing chemotherapy. Patients will be classified into CHM or non-CHM groups based on whether they received CHM during chemotherapy. Using generalized estimating equations or repeated measures ANOVA, we will assess differences in white blood cell counts, absolute neutrophil counts, immune cells, and programmed cell death protein 1 (PD-1) expression levels between the 2 groups. RESULTS: This study was approved by the research ethics committee of Hualien Tzu Chi Hospital (IRB 110-168-A). The enrollment process began in September 2021 and will stop in December 2024. A total of 140 patients will be recruited. Data cleaning and analysis are expected to finish in the middle of 2025. CONCLUSIONS: Traditional Chinese medicine is the most commonly used complementary medicine, and it has been reported to significantly alleviate chemotherapy-related side effects. This study's findings may contribute to developing effective interventions targeting chemotherapy-related neutropenia among patients with breast cancer in clinical practice. TRIAL REGISTRATION: International Traditional Medicine Clinical Trial Registry ITMCTR2023000054; https://tinyurl.com/yc353hes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55662.
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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 | 0.030 | 0.031 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.047 | 0.008 |
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".