Effect of silicone gel versus Contractubex or corticosteroid phonophoresis for post-burn hypertrophic scars: a single-blind randomized controlled trial
Bibliographic record
Abstract
Introduction Hypertrophic scars and keloids are physically and mentally disturbing, and can cause pain and itching. Various treatment is utilized to diminish or counteract scarring. The purpose of this study was to compare the effect of silicone gel, Contractubex gel, and corticosteroid phonophoresis for post-burn hypertrophic scars. Methods A prospective, single-blind, randomized, controlled study with pretest-posttest design was performed; 45 patients aged 20–45 years with hypertrophic scars, 2–4 months post thermal burn were involved. They were randomly assigned to 3 groups: group A (15 patients, 8 males and 7 females) received phonophoresis with silicone gel, group B (15 patients, 8 males and 7 females) received phonophoresis with Contractubex gel, and group C (15 patients, 9 males and 6 females) received corticosteroid phonophoresis. All treatment interventions were applied at the frequency of 3 sessions/week for 24 weeks. The participants were recruited from the Kasr El-Ani hospital outpatient clinic to be treated in the outpatient clinic, Faculty of Physical Therapy, Cairo University. Results Outcome measures were taken with the modified Vancouver Scar Scale (VSS) before the treatment, and 12 and 24 weeks after the intervention. The comparison among the 3 groups revealed a highly significant decrease in modified VSS total score in group A compared with group B and group C (p < 0.01), as well as in group B compared with group C (p < 0.001). Conclusions Silicone gel phonophoresis is a more effective method for post-burn hypertrophic scar management than Contractubex gel phonophoresis or corticosteroid phonophoresis.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".