Comparison of the Effect of Platelets Rich Fibrin, Heparin and Insulin in the Healing of Skin Graft Donor Site
Bibliographic record
Abstract
Background: Wound healing after burns or trauma can be enhanced by therapies like split-thickness skin graft (STSG), insulin, platelet-rich fibrin (PRF), and heparin, which promote tissue regeneration through growth factors stimulation and stabilization. Aim: The aim of this work was to compare the healing time needed for skin graft donor sites between PRF, Heparin and Insulin in patients eligible for STSG. Methods: This prospective intervention study was carried out on 96 burned patients who have deep burns and in need of skin grafting, raw areas suitable for split thickness (skin graft) coverage, donor site: both thighs and grafts: medium split thickness graft. Patients were divided into four groups contingent upon treatment injected into the donor site at 10 random intradermal points: Group 1: Received 1 ml of PRF. Group 2: Received long-acting insulin at a dose of 10 IU (10 IU/ml). Group 3: Received heparin 1000 IU (1000 IU/ml) subcutaneously. Group 4: Received 1 ml of normal saline. Results: There was a significant difference among the groups concerning post-operative random blood sugar, percentage of healing at days 7, 14, and 21, overall healing time, vascularity, pliability, height, and Vancouver scar scale (P<0.001). There was no significant difference among the studied groups concerning comorbidities, site of STSG, pre-operative RBS, and pigmentation. Conclusions: the superiority of PRF and insulin over heparin in the healing of the donor site with decreased healing time. Infection, bleeding, itching and foul odour of semipermeable dressing were significantly higher in heparin group.
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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 | 0.001 | 0.001 |
| 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".