Integrated Chinese Herbal Medicine and Western Medicine on the Survival in Patients with Colorectal Cancer: A Retrospective Study of Medical Records
Bibliographic record
Abstract
Recent studies suggested that Traditional Chinese Medicine could play a beneficial role in conventional cancer treatment. The aim of this retrospective cohort study was to investigate the effect of Chinese herbal medicine (CHM) combined with Western medicine on the survival of patients with colorectal cancer. A retrospective cohort study was conducted on patients with newly diagnosed colorectal cancer identified from the Dalin Tzu Chi Hospital Cancer Registry Database in 2004-2014. Combining with the medical records of the study hospital, patients were classified into CHM users and CHM nonusers. Kaplan-Meier analyses and Cox proportional hazards regression analyses were used to investigate the survival between CHM users and CHM nonusers. A total of 535 patients with colorectal cancer were included in the study with 147 of them were CHM users. The log-rank test for Kaplan-Meier survival curve revealed a statistically significant difference between the survival of CHM users and CHM nonusers (P=0.006). Results from multivariate Cox regression analysis showed that CHM use was significantly associated with better survival (adjusted hazard ratio = 0.54, 95% CI = 0.38 to 0.77). In addition, the CHM formulae Jia Wei Xiao Yao San, Zhi Bah Di Huang Wan, Ping Wei San, and Qui Pi Tang were significantly associated with better survival. In conclusion, findings from this retrospective cohort study indicated that integrated CHM and Western medicine could improve survival in patients with colorectal cancer. Additional research on integrating TCM with Western medicine to improve cancer survival is warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".