Oral Chinese Herbal Medicine as Maintenance Treatment after Chemotherapy for Advanced Non-Small-Cell Lung Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: The concept of maintenance therapy in cancer treatment is currently under debate because of modest survival benefits, added toxicity, economic considerations, and quality-of-life concerns. Traditional Chinese Medicine (TCM) is widely used in China for cancer patients, offering the advantages of low toxicity and enhancement of quality of life. However, no systematic reviews or meta-analyses have assessed the role of TCM as maintenance treatment for non-small-cell lung carcinoma. Methods: We searched the Chinese Biomedical Literature Database, the China National Knowledge Infrastructure, PubMed, EMBASE, and the Cochrane Library databases for all eligible studies. The endpoints were overall survival (OS), progression-free survival (PFS), the 1-year and 2-year survival rates, and performance status. Our meta-analysis used a fixed-effects model and a random-effects model for heterogeneity in the Stata software application (version 11.0: StataCorp LP, College Station, TX, U.S.A.), with the results expressed as hazard ratios (HRS) or risk ratios (RRS), with their corresponding 95% confidence intervals (95% CIS). Results: Sixteen randomized studies representing 1150 patients met the inclusion criteria. Compared with best supportive care, observation, or placebo, TCM as maintenance treatment was associated with a significant increase in OS (HR: 0.49; 95% CI: 0.35 to 0.68; p < 0.001), PFS (HR: 0.66; 95% CI: 0.51 to 0.84; p = 0.001), and 2-year survival rate (RR: 0.63; 95% CI: 0.44 to 0.92, p = 0.017), and a significant improvement in performance status (RR: 0.68; 95% CI: 0.61 to 0.75; p < 0.001). Conclusions: For patients who show non-progression—including stable disease, partial response, or complete response—after first-line chemotherapy, including those with poor quality of life, oral Chinese herbal medicine can be considered an efficient and safe maintenance therapy strategy.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".