The utility of onion extract gel containing topical allantoin and heparin after surgical treatment of upper extremity burn scars
Bibliographic record
Abstract
Background: The development of hypertrophic scars after burns can lead to esthetic as well as functional disorders. The aim of the study was to determine the functional and cosmetic effects of Contractubex and reg; (onion extract, heparin, allantoin) gel applied in burn scar patients after surgery for scar excision and skin grafts.\nPatients and Methods: The study included seven male patients who presented to our clinic between 2005 and 2012 for the treatment of hypertrophic burn scar and were administered either single or combined medical hypertrophic scar treatments. Patients who had scars on the right upper extremity were included in group 1 and those with left upper extremity scars in group 2. In group 1, all scars were excised and closed with medium thickness skin graft. After surgery, the onion extract gel was applied to the right upper extremities. In group 2, only surgical treatment was applied. The results were evaluated with Vancouver scar scale.\nResults: Vascularity, flexibility, and height of the scars improved significantly in both groups. In addition, hyperpigmentation was observed on the skin grafts of all patients. Scar flexibility was less often observed in patients' left upper extremities. However, no statistical difference between groups 1 and 2 was found.\nConclusion: Although no significant difference was obtained with Contractubex gel treatment in this study, cosmetic and functional success can be achieved through excision of the scar and use of medium thickness skin graft in patients with upper extremity hypertrophic burn scarring that is resistant to conservative treatments. [Hand Microsurg 2014; 3(3.000): 74-79]
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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".