Trends in Kampo Medicine Usage as Supportive Care During Anticancer Drug Treatment in Japanese Patients: A Nationwide Cohort Analysis from Fiscal Years 2015 to 2021
Bibliographic record
Abstract
The adverse effects of anticancer drugs significantly impact the quality of life of patients undergoing chemotherapy, necessitating evidence-based supportive therapies. In Japan, Kampo medicines, traditional Japanese herbal therapies used for relief of various symptoms, have been widely used as complementary and alternative treatments for cancer, despite limited evidence regarding their efficacy and safety. Thus, we investigated the actual use of Kampo medicines as supportive care in patients undergoing anticancer drug treatment and evaluated the trends in prescription according to year. We analyzed 89,141 cancer drug therapy cases registered in the Japan Medical Data Center database between April 2014 and July 2022, excluding those with a history of Kampo medicine prescriptions before the first prescription of antineoplastic drugs. We assessed the trends in prescription according to sex, age group (<50, 50-74, and ≥75 years), and cancer type subgroup using the Cochran-Armitage trend test. Approximately 23.7% of patients were prescribed Kampo medicines during anticancer drug treatment. Since 2014, a decrease in the prescription of Kampo medicines during anticancer treatment has been observed regardless of sex, age, or cancer type. These findings suggest that recent negative reports on the efficacy and safety of Kampo medicines in cancer care may have influenced this trend.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".