[Treatment of facial pigmentation after burns with traditional Chinese medicine mask and skin care].
Bibliographic record
Abstract
OBJECTIVE: To observe the effects of traditional Chinese medicine mask combined with skin care in the treatment of facial pigmentation after burns. METHODS: Forty-one patients with facial pigmentation after burns hospitalized from January 2009 to June 2010 were enrolled and divided into treatment group (n = 26, odd number) and control group (n = 15, even number) according to the visiting order. After cleaning, massaging, and steam spraying to faces, patients in treatment and control groups were respectively treated with traditional Chinese medicine mask developed by physicians in our hospital and common commercial beauty mask. Masks were removed when they became completely dry. The treatment was repeated every other day. Ten times made up a course of treatment. Three consecutive courses were carried out on each patient. Before treatment and at the end of each course, the skin color differences were measured with Vancouver Pigmented Scar Scale; image gray scale value of pigmented skin was measured with image analysis technique. Satisfaction degree acknowledged by both patients and physicians were recorded at the end of each course; adverse effects were recorded; and the overall efficacy between two groups was compared. Data were processed with chi-square test or t test. RESULTS: Skin color differences, image gray scale value of patients in treatment group were close to those in control group before treatment (with t value respectively 0.800 and 0.694, P values all above 0.05). Skin color differences, image gray scale value, and satisfaction degree acknowledged by both patients and physicians in treatment group were better than those in control group at the end of each course. At the end of the third course of treatment, the skin color differences in treatment and control groups scored 0.5 ± 0.4 and 1.1 ± 0.6, respectively, with image gray scale value of 55 ± 5 and 66 ± 6, respectively, which were statistically different from each other (with t value respectively 3.389 and 5.102, P values all below 0.01). The overall efficacy of the treatment group was 92.3%, which was much better than that of the control group (53.3%, χ(2) = 6.31, P < 0.05). No allergy caused by the traditional Chinese medicine mask was observed during the treatment. CONCLUSIONS: The traditional Chinese medicine mask combined with skin care is effective for the treatment of facial pigmentation after burns.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".