Reducing Postsurgical Exudate in Breast Cancer Patients by Using San Huang Decoction to Ameliorate Inflammatory Status: A Prospective Clinical Trial
Bibliographic record
Abstract
Background Reducing inflammatory factors in wound exudate is a promising treatment approach for healing wounds in postsurgical breast cancer patients. Traditional Chinese Medicine (tcm) treatments have been shown to be beneficial and safe for optimal regulation of oxidative stress during the postoperative period. In the present clinical trial, we evaluated the effectiveness of a promising Chinese herbal formula, San Huang decoction [shd (Radix astragali, Radix et rhizoma rhei, and Rhizoma curcuma longa, 3:1:1; supplemental Table 1)], on wound inflammatory response after mastectomy. Methods The study randomized 30 patients with breast cancer who fulfilled the inclusion and exclusion criteria to either a treatment (n = 15) or a control group (n = 15). Patients in the treatment group received liquid shd, taken twice daily with or without food. Treatment was given for 1 day before surgery and for 7 days postoperatively. Participants in the control group received a placebo on the same schedule as the treatment group. Outcomes measured in every subject included clinical tcm and wound inflammation symptom scores, daily and total amounts of drainage fluid, and levels of inflammatory factors in the exudate [tumour necrosis factor α (tnf-α), interleukins 6 (il-6), 8 (il-8), and 2R (il-2R), human C-reactive protein (crp)] at 2 h and on days 1, 3, and 7 postoperatively. Results The total amount of drainage fluid over 7 days was significantly lower in the treatment group (572.20 ± 93.95 mL) than in the control group (700.40 ± 107.38 mL). The tcm symptom score was also lower in treatment group (day 7: 1.87 ± 0.83 vs. 4.80 ± 3.61, p = 0.049), as was the inflammatory symptom score (day 7: 0.67 ± 0.72 vs. 3.67 ± 2.50, p = 0.001). Levels of tnf-α, il-6, il-8, il-2R, and crp in drainage fluid were significantly lower with shd treatment. Conclusions Perioperative treatment with shd effectively lessened postoperative exudate and ameliorated inflammatory symptoms in patients who underwent surgery for breast cancer.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".