"Impact of Local Insulin Injection on Split Thickness Skin Graft Donor Site Healing"
Bibliographic record
Abstract
Background: Split thickness skin graft (STSG) remains the most common surgical technique to cover skin defects. However, the healing of its donor area is of paramount importance. Multiple local drugs are frequently used to enhance its epithelialization. Systemic Insulin was proved to be beneficial for chronic wound healing such as pressure sores and diabetic ulcers. But the impact of local insulin injection was scarcely studied before as regard wound healing. Therefore, this study aimed to evaluate the effect of intradermal insulin injection on the donor of harvested split thickness skin graft (STSG). Patients and Methods: In this study, 40 patients; 18 males and 22 females with skin loss planned to be covered by STSG were selected. Pregnant, lactating, pre-diabetics and diabetic patients were excluded. The dimensions of the raw area ranged from 5 – 10 cm for maximum height and width. The thigh was used as a donor area and divided by a horizontal line into test (upper proximal half) and control (lower distal half) areas. Then, a 10 IU of long-acting insulin (Lantus ®) was intra-dermally injected in the test area. Random blood sugar was measured 6 hours postoperatively. The rate of epithelialization on days 14 and 21 was analyzed by a special software (Image J®) and the scar quality was also evaluated by Vancouver scar scale (VSS). Results: For the test area, the rate of epithelialization was significantly faster on days 14 and 21 as compared with the control area. But there was no statistical difference between both areas regarding the scar quality when using Vancouver scar scale.Conclusion: Intradermal injection of insulin in donor sites of split thickness skin graft accelerates its healing which allows early re-harvesting of grafts from same donor sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".