Bibliographic record
Abstract
Objective To investigate the efficacy of Kenacort-A on hypertrophic scar by measuring the chroma value of hypertrophic scar before and after Kenacort-A treatments. Methods Thirty-six patients with hypertrophic scar were enrolled in an own controll study. Two masses of scars taken consecutively on the same area of every patient and the masses of scars were divided into therapy group and control group. The scars in the therapy group were administrated with 1% Kenacort-A injection 4 mg/cm2 for 4 times injection with 2 weeks intervals. The scars in the control group received equal doses of lidocaine injection for 4 times with 2 weeks intervals. Chroma values of both groups were determined by Spectrophotometer CM-2600 d before and after treatments at 4 weeks, and Vancouver scar scores were used to synthetically evaluated the scars at the same time. Results All the patients were followed up for 1 to 3 months. Physiological symptoms of itch and aching in therapy group were relieved, hyperaemia of scar tissue were lessened and scar color turned light. The L values increased, the a values and the Vancouver scar scores decreased, which had statistical significance compared with those before treatment (P0.01). In control group, the post-treatment chroma values weren′t statistical significant different from the pre-treatment values (P0.05). There were significant differences between the therapy group and the control groups regarding the changes of every chroma values (P0.01). Conclusion Chroma values of hypertrophic scar tissue changed significantly after Kenacort-A treatments, therefore, Kenacort-A is one of the effective drugs for hypertrophic scar.
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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) | 0.001 | 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".