Effect of a twin-herb formula for the Treatment of Chronic Non-healing Ulcers: a Clinical Study
Bibliographic record
Abstract
Forty-four patients (27 males and 17 females) with Chronic Non-healing Ulcers that had been refractory to conventional treatment were enrolled in a single arm pre-and post-treatment clinical trial.A twin-herb formula was provided for the treatment for the 6-month study period.All conventional treatment of wound care remained unchanged.Their ulcers were assessed every month.The ulcer sizes greatest length and width was measured at baseline.The target ulcer was assessed at baseline and then every month for 6 months or healed.The mean age of patients was 66.6 (38.0-93.0)years.The mean surface area of ulcers was 28.0±77.1 cm 2 and 14.8±55.0cm 2 at baseline and six months of study respectively.Differences in ulcer area between the initial and fi nal visit were signifi cant (p<0.001).The mean healing time was 149 days (median 167 days).Our results showed that the two-herb recipe (NF3) could reduce the wound size about 50% during 6 months period.36.7% ulcers were healed.No adverse side effects were founded in our patients during study period.The twin-herb formula showed promising results in initiating the healing process in chronic nonhealing ulcers.It was found to be well tolerated and safe to use.However, further clinical trials should be performed involving a control group to verify these data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".